Voices of CS: Didac Suris

PhD student Didac Suris talks about his research and winning a prestigious Microsoft Research Fellowship.

 

“So, this is a rough idea for modeling trajectories and I need your feedback,” said Didac Suris to the room while his teammates looked at him over bowls of Chinese food. “I literally just thought of this two days ago.”

It is the first week that working lunch meetings can resume at Columbia. Suris, along with other members of the computer vision lab, immediately took advantage of it. As they settle down into the meeting, Suris talks about his research proposal, and his audience exchange ideas with him in between bites of food. The last time this happened was two years ago.

“We came back in the Fall and it is good to be back in the office,” said Didac Suris, a third-year PhD student advised by Carl Vondrick. “Collaborating with teammates and just being out has worked wonders for my productivity which has skyrocketed compared to when working alone, or from home.”

Suris (center) with the computer vision lab members

Suris can be found in an office in CEPSR working on research projects that study computer vision and machine learning. The projects focus on training machines to interact and observe their surroundings, including his work on predicting what will happen next in a video. This is in line with his long-term goal of creating systems that can model video more appropriately and help predict the future actions of a video, which will be useful in autonomous vehicles, human-robot interaction, broadcasting of sports events, and assistive technology.

Suris was recently named a Microsoft Research Fellow. The research he has done while at Columbia focuses on computer vision and building systems that can learn on their own which is very different from what he studied in undergrad, telecommunications at the Polytechnic University of Catalunya in Barcelona, Spain. We caught up with Suris to ask about how his PhD is going and winning the fellowship.

Q: What was your journey to Columbia? How did you pivot from telecommunications to applying for a PhD in computer vision?
It was only during my masters, when I started doing research on computer vision, that I started to consider doing a PhD. The main reason I’m doing a PhD is because I believe it is the best way to push myself intellectually.

I really recommend doing research in different places before starting a PhD. Before starting at Columbia, I did research in three different universities, which prepared me for my current research. These experiences helped me to 1) understand what research is about, and 2) understand that different research groups work differently, and get the best out of each one.


Q: What drew you to machine learning and artificial intelligence?
One of the characteristic aspects of this field is how fast it is evolving, and how impressive the research results have been in the last decade. I don’t think there was a specific moment where I decided to do research on this topic, I would say there was a series of circumstances that led me here, including the fact that I was originally interested in artificial intelligence in the first place, of course.


Q: Why did you decide to focus on computer vision?
There is a lot of information online because of the vast amount of videos, images, text, audio, and other forms of data. But the thing is the majority of this information is not labeled clearly. For example, we do not have information about the actions taking place in every YouTube video. But we can still use the information in the YouTube video to learn about the world.

We can teach a computer to relate the audio in a video to the visual content in a video. And then we can relate all of this to the comments on the YouTube video to learn associations between all of these different signals, and help the computer understand the world based on these associations. I want to be able to use any and all information out there to develop systems that will train computers to learn with minimal human supervision.


Q: What sort of research questions or issues do you hope to answer?
There is a lot of data about the world on the Internet – billions of videos are recorded every day across the world. My main research question is how can we make sense of all of this raw video content.


Q: What was the thesis proposal that you submitted for the Microsoft PhD?
The proposal was called “Video Hyperboles.” The idea is to model long videos (most of the literature nowadays is on very short clips, not long-format videos) by modeling their temporal hierarchy. For example, the action of “cutting an onion” is composed of the subactions “grabbing a knife”, “pressing the knife”, “gathering the pieces.” This forms a temporal hierarchy, in which the action “cutting an onion” is higher in the hierarchy, and the subactions are lower in the hierarchy. Hierarchies can be modeled in a geometric space called Hyperbolic Space, and thus the name “Video Hyperboles.”

I have not been working on the project directly, but I am building up pieces to eventually be able to achieve something like what I described in the proposal. I work on related topics, with the general direction of creating a video representation (for example, a hierarchy) that allows us to model video more appropriately, and helps us predict the future of a video. And I will work on this for the rest of my PhD.


Q: What is your advice to students on how to navigate their time at Columbia? If they want to do research what should they know or do to prepare?
Research requires a combination of abilities that may take time to develop: patience, asking the right questions, etc. So experience is very important. My main advice would be to try to do research as soon as possible. Experience is very necessary to do research but is also important in order to decide whether or not research is for you. It is not for everyone, and the sooner you figure that out, the better.


Q: Is there anything else that you think people should know about getting a PhD?
Most of the time, a PhD is sold as a lot of pain and suffering, as working all day every day, and being very concerned about what your advisor will think of you. At least this is how it is in our field. It is sometimes seen as a competition to be a great and prolific researcher, too. And I don’t see it like that – you can enjoy (or hate) your PhD the same way you enjoy any other career path. It is all about finding the correct topics to work on, and the correct balance between research and personal life.

Shuran Song and Huy Ha Win the Best System Paper at CoRL 2021

Research papers from the department were accepted to the Conference on Robot Learning 2021 (CoRL) and the Best System Paper award was given to Assistant Professor Shuran Song and PhD student Huy Ha. 

Below are the abstracts and links to the papers: 

Best System Paper Award
FlingBot: The Unreasonable Effectiveness of Dynamic Manipulation for Cloth Unfolding
Huy Ha Columbia University and Shuran Song Columbia University

Abstract: 
High-velocity dynamic actions (e.g., fling or throw) play a crucial role in our everyday interaction with deformable objects by improving our efficiency and effectively expanding our physical reach range. Yet, most prior works have tackled cloth manipulation using exclusively single-arm quasi-static actions, which requires a large number of interactions for challenging initial cloth configurations and strictly limits the maximum cloth size by the robot’s reach range. In this work, we demonstrate the effectiveness of dynamic flinging actions for cloth unfolding with our proposed self-supervised learning framework, FlingBot. Our approach learns how to unfold a piece of fabric from arbitrary initial configurations using a pick, stretch, and fling primitive for a dual-arm setup from visual observations. The final system achieves over 80% coverage within 3 actions on novel cloths, can unfold cloths larger than the system’s reach range, and generalizes to T-shirts despite being trained on only rectangular cloths. We also finetuned FlingBot on a real-world dual-arm robot platform, where it increased the cloth coverage over 4 times more than the quasi-static baseline did. The simplicity of FlingBot combined with its superior performance over quasi-static baselines demonstrates the effectiveness of dynamic actions for deformable object manipulation.

 

Toward Robots That Learn To Summarize Their Actions In Natural Language: A Set Of Tasks
Chad DeChant Columbia University and Daniel Bauer Columbia University

Abstract:
Robots should be able to report in natural language what they have done. They should provide concise summaries, respond to questions about them, and be able to learn from the natural language responses they receive to their summaries. We propose that developing the capabilities for robots to summarize their actions is a new and necessary challenge that should be taken up by the robotic learning community. We propose an initial framework for robot action summarization, presented as a set of tasks that can serve as a target for research and a measure of progress.

 

The Boombox: Visual Reconstruction from Acoustic Vibrations
Boyuan Chen Columbia University, Mia Chiquier Columbia University, Hod Lipson Columbia University, and Carl Vondrick Columbia University

Abstract:
Interacting with bins and containers is a fundamental task in robotics, making state estimation of the objects inside the bin critical.

While robots often use cameras for state estimation, the visual modality is not always ideal due to occlusions and poor illumination. We introduce The Boombox, a container that uses sound to estimate the state of the contents inside a box. Based on the observation that the collision between objects and their containers will cause an acoustic vibration, we present a convolutional network for learning to reconstruct visual scenes. Although we use low-cost and low-power contact microphones to detect the vibrations, our results show that learning from multimodal data enables state estimation from affordable audio sensors. Due to the many ways that robots use containers, we believe the box will have a number of applications in robotics.

 

Voices of CS: Giannis Karamanolakis

Giannis Karamanolakis, a natural language processing and machine learning PhD student, talks about his research projects and how he is developing machine learning techniques for natural language processing applications.

Sitia, Greece

Can you talk about your background and why you decided to pursue a PhD?

I used to live in Greece and grew up in Sitia, a small town in Crete. In 2011, I left my hometown to study electrical and computer engineering at the National Technical University of Athens (NTUA).

At NTUA, taking part in machine learning (ML) research was not planned but rather a spontaneous outcome stemming from my love for music. The initial goal for my undergraduate thesis was to build an automatic music transcription system that converts polyphonic raw audio into music sheets. However, after realizing that such a system would not be possible to develop in a limited amount of time, I worked on the simpler task of automatically tagging audio clips with descriptive tags (e.g., “car horn” for audio clips where a car horn is sound). Right after submitting a new algorithm as a conference paper, I realized that I love doing ML research.

After NTUA, I spent one and a half years working as an ML engineer at a startup called Behavioral Signals, where we trained statistical models for the recognition of core emotions from speech and text data. After a few months of ML engineering, I found myself spending more time reading research papers and evaluating new research ideas on ML and natural language processing (NLP). By then, I was confident about my decision to pursue a PhD in ML/NLP.

What about NLP did you like and when did you realize that you wanted to do research on it?

I am fascinated by the ability of humans to understand complex natural language. At the moment of writing this response, I submitted the following 10-word query to Google: “when did you realize that you wanted to do research” by keeping quotation marks so that Google looks for exact matches only. Can you guess the number of the documents returned by Google that contain this exact sequence of 10 words?

The answer that I got was 0 (zero) documents, no results! In other words, Google, a company with huge collections of documents, did not detect any document that contains this specific sequence of words. Sentences rarely recur but humans easily understand the semantics of such rare sentences.

I decided to do research on NLP when I realized that current NLP algorithms are far away from human-level language understanding. As an example back from my time at Behavioral Signals, emotion classifiers were misclassifying sentences that contained sarcasm, negation, and other complex linguistic phenomena. I could not directly fix those issues (which are prevalent beyond emotion classification), which initially felt both surprising and frustrating, but then evolved into my excitement for research on NLP.

 

Why did you apply to Columbia and how was that process? 

The computer science department at Columbia was one of my top choices for several reasons, but I will discuss the first one. 

I was excited to learn about the joint collaboration between Columbia University and the New York City Department of Health and Mental Hygiene (DOHMH), on a project that aims to understand user-generated textual content in social media (e.g., Yelp reviews, tweets) for critical public health applications, such as detecting and acting on foodborne illness outbreaks in restaurants. I could see that the project would offer the unique opportunity to do research in ML and NLP and at the same time contribute to this important public application in collaboration with epidemiologists at DOHMH. Fortunately, I have been able to work on the project, advised by Professor Luis Gravano and Associate Professor Daniel Hsu.

Applying to Columbia and other American universities was quite a stressful experience. For many months, my days were filled with working for Behavioral Signals, studying hard for high scores in GRE and TOEFL exams (both of which were required at that time by all US universities), creating a short CV for the first time, and writing a distinct statement-of-purpose for each university. I am glad to observe the recent promising changes in the PhD application procedure for our department, such as waiving the GRE requirements and offering the Pre-submission Application Review (PAR) program, in which current PhD students help applicants improve their applications. (Both of which I would have liked to have been able to take advantage of.)

 

What sort of research questions or issues do you hope to answer?

My research in the past few years focuses on the following question: Can we effectively train ML classifiers for NLP applications with limited training data using alternative forms of human supervision?

An important limitation of current “supervised ML” techniques is that they require large amounts of training data, which is expensive and time-consuming to obtain manually. Thus, while supervised ML techniques (especially deep neural networks) thrive in standard benchmarks, it would be too expensive to apply to emerging real-world applications with limited labeled data.

Our work attempts to address the expensive requirement of manually labeled data through novel frameworks that leverage alternative, less expensive forms of human supervision. In sentiment classification, for example, we allow domain experts to provide a small set of domain-specific rules (e.g., “happy” keyword indicates positive sentiment, “diarrhea” is a symptom of food poisoning). Under low-resource settings with no labeled data, can we leverage expert-defined rules as supervision for training state-of-the-art neural networks? 

 

For your research papers, how did you decide to do research on those topics? How long did it take you to complete the work? Was it easy? 

For my first research project at Columbia, my goal was to help epidemiologists in health departments with daily inspections of restaurant reviews that discuss food poisoning events. Restaurant reviews can be quite long, with many irrelevant sentences surrounding the truly important ones that discuss food poisoning or relevant symptoms. Thus, we developed a neural network that highlights only important sentences in potentially long reviews and deployed it for inspections in health departments, where epidemiologists could quickly focus on the relevant sentences and safely ignore the rest.

The goal behind my next research projects was to develop frameworks for addressing a broader range of text-mining tasks, such as sentiment analysis and news document classification, and for supporting multiple languages without expensive labeled data for each language. To address this goal, we initially proposed a framework for leveraging just a few domain-specific keywords as supervision for aspect detection and later extended our framework for training classifiers across 18 languages using minimal resources.

Each project took about 6 months to complete. None of them were easy; each required substantial effort in reading relevant papers, discussing potential solutions with my advisors, implementing executable code, evaluating hypotheses on real data, and repeating the same process until we were all satisfied with the solutions and evaluation results. The projects also involved meeting with epidemiologists at DOHMH, re-designing our system to satisfy several (strict) data transfer protocols imposed by health departments, and overcoming several issues related to missing data for training ML classifiers.  

 

Your advisors are not part of the NLP group, how has that worked out for you and your projects?

It has worked great in my humble opinion. For the public health project, the expertise of Professor Gravano on information extraction, combined with the expertise of Professor Hsu on machine learning, and the technical needs of the project have contributed without any doubt to the current formulation of our NLP-related frameworks. My advisors’ feedback covers a broad spectrum of research, ranging from core technical challenges to more general research practices, such as problem formulation and paper writing. 

Among others, I appreciate the freedom I have been given for exploring new interesting research questions as well as the frequent and insightful feedback that helps me to reframe questions and forming solutions. At the same time, discussions with members of the NLP group, including professors and students, have been invaluable and have clearly influenced our projects.

 

What do you think is the most interesting thing about doing research?

I think it is the high amount of surprise it encompasses. For many research problems that I have tried to tackle, I started by shaping an initial solution in my mind but in the process discovered surprising findings that undoubtedly changed my way of thinking – such as that my initial solution did not actually work, simpler approaches worked better than more sophisticated approaches, data followed unexpected patterns, etc. These instances of surprise turned research into an interesting experience, similar to solving riddles or listening to jazz music. 

 

Please talk about your internships – the work you did, how was it, what did you learn?

In the summer of 2019, I worked at Amazon’s headquarters in Seattle with a team of more than 15 scientists and engineers. Our goal was to automatically extract and store knowledge about billions of products in a product knowledge graph. As part of my internship, we developed TXtract, a deep neural network that efficiently extracts information from product descriptions for thousands of product categories. TXtract has been a core component of Amazon’s AutoKnow, which provides the collected knowledge for Amazon search and product detail pages.

During the summer of 2020, I worked for Microsoft Research remotely from New York City (because of the pandemic). In collaboration with researchers at the Language and Information Technologies team, we developed a weak supervision framework that enables domain experts to express their knowledge in the form of rules and further integrates rules for training deep neural networks.

These two internships equipped me with invaluable experiences. I learned new coding tools, ML techniques, and research practices. Through the collaboration with different teams, I realized that even researchers who work on the same subfield may think in incredibly different ways, so to carry out a successful collaboration within a limited time, one needs to listen carefully, pre-define expected outcomes (with everyone in the team), and adapt fast.

 

Do you think your skills were improved by your time at Columbia? In which ways?

Besides having improved my problem-finding and -solving skills, I have expanded my presentation capabilities. In the beginning, I was frustrated when other people (even experienced researchers) could not follow my presentations and I was worried when I could not follow other presenters’ work. Later, I realized that if (at least part of) the audience is not able to follow a presentation, then the presentation is either flawed or has been designed for the wrong audience. 

Over the past four years, I have presented my work at various academic conferences and workshops, symposiums at companies, and student seminars, and after having received constructive feedback from other researchers, I can say that my presentation skills have vastly improved. Without any doubt, I feel more confident and can explain my work to a broader type of audience with diverse expertise. That said, I’m still struggling to explain my PhD topic to my family. 🙂

What has been the highlight of your time at Columbia? 

The first thing that comes to mind is the “Greek Happy Hour” that I co-organized in October 2019. More than 40 PhD students joined the happy hour, listened to Greek music (mostly “rempetika”), tasted greek specialties (including spanakopita), and all toasted loudly by saying “Γειά μας” (ya mas; the greek version of “cheers”).

 

Was there anything that was tough to handle while taking your PhD?

PhD Happy Hour in 2020

It is hard to work from home during a pandemic. A core part of my PhD used to involve multi-person collaborations, drawing illustrations on the whiteboards of the Data Science Institute, random chats in hallways, happy hours, and other social events. All these have been harder or impossible to retain during the pandemic. I miss it and look forward to enjoying it again soon.

Looking back, what would you have done differently?

If I could, I would have engaged in more discussions and collaborations, taken more classes, played more music, and slept less. 🙂

 

What is your advice to students on how to navigate their time at Columbia? If they want to do NLP research what should they know or do to prepare?

They should register for diverse courses; Columbia offers the opportunity to attend courses from multiple departments. They should reach out to as many people as possible and do not hesitate to email graduate students and professors. I love receiving emails from people that I haven’t met before, some of which stimulated creative collaborations. 

For those that want to do NLP research (which I highly recommend–subjectively speaking), you should contact me or any person in the NLP group.

 

What are your plans after Columbia?

I plan to continue working on research, either as a faculty member or in an industry research and development department.

 

Is there anything else that you think people should know?

Columbia offers free and discounted tickets to museums and performances around New York City, even virtual art events. I personally consider New York as the “state-of-the-art”.

7 Papers Accepted to COLT 2021

Research from the department was accepted to the 34th Annual Conference on Learning Theory (COLT2021). The conference highlights research on the theoretical aspects of machine learning.

Below are the abstracts and links to the accepted papers

Size and Depth Separation in Approximating Natural Functions with Neural Networks
Gal Vardi Weizmann Institute of Science, Daniel Reichman Worcester Polytechnic Institute, Toniann Pitassi Columbia University, Ohad Shamir Weizmann Institute of Science

When studying the expressive power of neural networks, a main challenge is to understand how the size and depth of the network affect its ability to approximate real functions. However, not all functions are interesting from a practical viewpoint: functions of interest usually have a polynomially bounded Lipschitz constant, and can be computed efficiently. We call functions that satisfy these conditions “benign” and explore the benefits of size and depth for approximation of benign functions with ReLU networks. As we show, this problem is more challenging than the corresponding problem for non-benign functions. We give complexity-theoretic barriers to showing depth-lower bounds: Proving existence of a benign function that cannot be approximated by polynomial-sized networks of depth 4 would settle longstanding open problems in computational complexity. It implies that beyond depth 4 there is a barrier to showing depth-separation for benign functions, even between networks of constant depth and networks of nonconstant depth. We also study size separation, namely, whether there are benign functions that can be approximated with networks of size O(s(d)), but not with networks of size O(s 0 (d)). We show a complexity-theoretic barrier to proving such results beyond size O(d log2 (d)), but also show an explicit benign function, that can be approximated with networks of size O(d) and not with networks of size o(d/ log d). For approximation in the L∞ sense we achieve such separation already between size O(d) and size o(d). Moreover, we show superpolynomial size lower bounds and barriers to such lower bounds, depending on the assumptions on the function. Our size-separation results rely on an analysis of size lower bounds for Boolean functions, which is of independent interest: We show linear size lower bounds for computing explicit Boolean functions (such as set disjointness) with neural networks and threshold circuits.

 

Learning sparse mixtures of permutations from noisy information
Rocco Servedio Columbia University, Anindya De University of Pennsylvania, Ryan O’Donnell Carnegie Mellon University

We study the problem of learning an unknown mixture of k permutations over n elements, given access to noisy samples drawn from the unknown mixture. We consider a range of different noise models, including natural variants of the “heat kernel” noise framework and the Mallows model. We give an algorithm which, for each of these noise models, learns the unknown mixture to high accuracy under mild assumptions and runs in n O(log k) time. Our approach is based on a new procedure that recovers an unknown mixture of permutations from noisy higher-order marginals.

 

Learning and testing junta distributions with subcube conditioning
Xi Chen Columbia University, Rajesh Jayaram Carnegie Mellon University, Amit Levi University of Waterloo, Erik Waingarten Stanford University

We study the problems of learning and testing junta distributions on {−1, 1} n with respect to the uniform distribution, where a distribution p is a k-junta if its probability mass function p(x) depends on a subset of at most k variables. The main contribution is an algorithm for finding relevant coordinates in a k-junta distribution with subcube conditioning Bhattacharyya and Chakraborty (2018); Canonne et al. (2019). We give two applications: • An algorithm for learning k-junta distributions with O˜(k/2 ) log n + O(2k/2 ) subcube conditioning queries, and • An algorithm for testing k-junta distributions with O˜((k + √ n)/2 ) subcube conditioning queries. All our algorithms are optimal up to poly-logarithmic factors. Our results show that subcube conditioning, as a natural model for accessing high-dimensional distributions, enables significant savings in learning and testing junta distributions compared to the standard sampling model. This addresses an open question posed by Aliakbarpour et al. (2016).

 

Survival of the strictest: Stable and unstable equilibria under regularized learning with partial information
Emmanouil Vasileios Vlatakis-Gkaragkounis Columbia University, Angeliki Giannou National Technical University of Athens, Panayotis Mertikopoulos Univ. Grenoble Alpes, CNRS, Inria, Grenoble INP, LIG, 38000 Grenoble, France & Criteo AI Lab

In this paper, we examine the Nash equilibrium convergence properties of no-regret learning in general N-player games. For concreteness, we focus on the archetypal “follow the regularized leader” (FTRL) family of algorithms, and we consider the full spectrum of uncertainty that the players may encounter – from noisy, oracle-based feedback, to bandit, payoff-based information. In this general context, we establish a comprehensive equivalence between the stability of a Nash equilibrium and its support: a Nash equilibrium is stable and attracting with arbitrarily high probability if and only if it is strict (i.e., each equilibrium strategy has a unique best response). This equivalence extends existing continuous-time versions of the “folk theorem” of evolutionary game theory to a bona fide algorithmic learning setting, and it provides a clear refinement criterion for the prediction of the day-to-day behavior of no-regret learning in games.

 

Reconstructing weighted voting schemes from partial information about their power indices
Emmanouil Vasileios Vlatakis-Gkaragkounis Columbia University, Huck Bennett Columbia University, Anindya De Columbia University, Rocco Servedio Columbia University

A number of recent works (Goldberg, 2006; O’Donnell and Servedio, 2011; De et al., 2017, 2014) have considered the problem of approximately reconstructing an unknown weighted voting scheme given information about various sorts of “power indices” that characterize the level of control that individual voters have over the final outcome. In the language of theoretical computer science, this is the problem of approximating an unknown linear threshold function (LTF) over {−1, 1} n given some numerical measure (such as the function’s n “Chow parameters,” a.k.a. its degree-1 Fourier coefficients, or the vector of its n Shapley indices) of how much each of the n individual input variables affects the outcome of the function. In this paper we consider the problem of reconstructing an LTF given only partial information about its Chow parameters or Shapley indices; i.e. we are given only the Chow parameters or the Shapley indices corresponding to a subset S ⊆ [n] of the n input variables. A natural goal in this partial information setting is to find an LTF whose Chow parameters or Shapley indices corresponding to indices in S accurately match the given Chow parameters or Shapley indices of the unknown LTF. We refer to this as the Partial Inverse Power Index Problem. Our main results are a polynomial time algorithm for the (ε-approximate) Chow Parameters Partial Inverse Power Index Problem and a quasi-polynomial time algorithm for the (ε-approximate) Shapley Indices Partial Inverse Power Index Problem.

 

On the Approximation Power of Two-Layer Networks of Random ReLUs
Daniel Hsu Columbia University, Clayton H Sanford Columbia University, Rocco Servedio Columbia University, Emmanouil Vasileios Vlatakis-Gkaragkounis Columbia University

This paper considers the following question: how well can depth-two ReLU networks with randomly initialized bottom-level weights represent smooth functions? We give near-matching upper and lower-bounds for L2-approximation in terms of the Lipschitz constant, the desired accuracy, and the dimension of the problem, as well as similar results in terms of Sobolev norms. Our positive results employ tools from harmonic analysis and ridgelet representation theory, while our lower-bounds are based on (robust versions of) dimensionality arguments.

 

Weak learning convex sets under normal distributions
Anindya De Columbia University, Rocco Servedio Columbia University

This paper addresses the following natural question: can efficient algorithms weakly learn convex sets under normal distributions? Strong learnability of convex sets under normal distributions is well understood, with near-matching upper and lower bounds given in Klivans et al. (2008), but prior to the current work nothing seems to have been known about weak learning. We essentially answer this question, giving near-matching algorithms and lower bounds. For our positive result, we give a poly(n)-time algorithm that can weakly learn the class of convex sets to advantage Ω(1/ √ n) using only random examples drawn from the background Gaussian distribution. Our algorithm and analysis are based on a new “density increment” result for convex sets, which we prove using tools from isoperimetry. We also give an information-theoretic lower bound showing that O(log(n)/ √ n) advantage is best possible even for algorithms that are allowed to make poly(n) many membership queries.

Papers from CS Researchers Accepted to SIGDIAL 2021

Research from the department was presented at the 22nd Annual Meeting of the Special Interest Group on Discourse and Dialogue (SIGDIAL 2021). The conference is a forum for academic and industry researchers to discuss their work on discourse and dialogue including discourse processing, dialogue systems, corpora, tools, and methodology.

Professor Julia Hirschberg was one of the invited keynote speakers and during her lecture she talked about how computer systems can encourage user trust for recommender systems, knowledge-delivery systems, and dialogue systems.

Below are the links to the accepted papers and abstracts.

What to Fact-Check: Guiding Check-Worthy Information Detection in News Articles Through Argumentative Discourse Structure
Tariq Alhindi Columbia University, Brennan McManus Columbia University, and Smaranda Muresan Columbia University

Most existing methods for automatic fact checking start with a precompiled list of claims to verify. We investigate the understudied problem of determining what statements in news articles are worthy to factcheck. We annotate the argument structure of 95 news articles in the climate change domain that are fact-checked by climate scientists at climatefeedback.org. We release the first multi-layer annotated corpus for both argumentative discourse structure (argument components and relations) and for fact checked statements in news articles. We discuss the connection between argument structure and check-worthy statements and develop several baseline models for detecting checkworthy statements in the climate change domain. Our preliminary results show that using information about argumentative discourse structure shows slight but statistically significant improvement over a baseline of local discourse structure.

 

Improving Named Entity Recognition in Spoken Dialog Systems By Context and Speech Pattern Modeling
Minh Nguyen University of California, Davis and Zhou Yu Columbia University

While named entity recognition (NER) from speech has been around as long as NER from written text has, the accuracy of NER from speech has generally been much lower than that of NER from text. The rise in popularity of spoken dialog systems such as Siri or Alexa highlights the need for more accurate NER from speech because NER is a core component for understanding what users said in dialogs. Deployed spoken dialog systems receive user input in the form of automatic speech recognition (ASR) transcripts, and simply applying NER model trained on written text to ASR transcripts often leads to low accuracy because compared to written text, ASR transcripts lack important cues such as punctuation and capitalization. Besides, errors in ASR transcripts also make NER from speech challenging. We propose two models that exploit dialog context and speech pattern clues to extract named entities more accurately from open-domain dialogs in spoken dialog systems. Our results show the benefit of modeling dialog context and speech patterns in two settings: a standard setting with random partition of data and a more realistic but also more difficult setting where many named entities encountered during deployment are unseen during training.

 

Evaluation of In-Person Counseling Strategies to Develop Physical Activity Chatbot for Women
Kai-Hui Liang Columbia University, Patrick Lange University of California, Davis, Yoo Jung Oh University of California, Davis, Jingwen Zhang University of California, Davis, Yoshimi Fukuoka University of California, San Francisco, and Zhou Yu Columbia University

Artificial intelligence chatbots are the vanguard in technology-based intervention to change people’s behavior. To develop intervention chatbots, the first step is to understand natural language conversation strategies in human conversation. This work introduces an intervention conversation dataset collected from a real-world physical activity intervention program for women. We designed comprehensive annotation schemes in four dimensions (domain, strategy, social exchange, and taskfocused exchange) and annotated a subset of dialogs. We built a strategy classifier with context information to detect strategies from both trainers and participants based on the annotation. To understand how human intervention induces effective behavior changes, we analyzed the relationships between the intervention strategies and the participants’ changes in the barrier and social support for physical activity. We also analyzed how the participant’s baseline weight correlates to the amount of occurrence of the corresponding strategy. This work lays the foundation for developing a personalized physical activity intervention bot.

 

DialogStitch: Synthetic Deeper and Multi-Context Task-Oriented Dialogs
Satwik Kottur Facebook AI, Chinnadhurai Sankar Facebook AI, Zhou Yu Columbia University, and Alborz Geramifard Facebook AI

Real-world conversational agents must effectively handle long conversations that span multiple contexts. Such context can be interspersed with chitchat (dialog turns not directly related to the task at hand), and potentially grounded in a multimodal setting. While prior work focused on the above aspects in isolation, there is a lack of a unified framework that studies them together. To overcome this, we propose DialogStitch, a novel framework to seamlessly ‘stitch’ multiple conversations and highlight these desirable traits in a task-oriented dialog. After stitching, our dialogs are provably deeper, contain longer-term dependencies, and span multiple contexts, when compared with the source dialogs— all by leveraging existing human annotations! Though our framework generalizes to a variety of combinations, we demonstrate its benefits in two settings: (a) multimodal, image-grounded conversations, and, (b) task-oriented dialogs fused with chit-chat conversations. We benchmark state-of-the-art dialog models on our datasets and find accuracy drops of (a) 12% and (b) 45% respectively, indicating the additional challenges in the stitched dialogs. Our code and data are publicly available.

 

Annotation Inconsistency and Entity Bias in MultiWOZ
Kun Qian Columbia University, Ahmad Beirami Facebook AI, Zhouhan Lin Facebook AI, Ankita De Facebook AI, Alborz Geramifard Facebook AI, Zhou Yu Columbia University, and Chinnadhurai Sankar Facebook AI

MultiWOZ (Budzianowski et al., 2018) is one of the most popular multi-domain taskoriented dialog datasets, containing 10K+ annotated dialogs covering eight domains. It has been widely accepted as a benchmark for various dialog tasks, e.g., dialog state tracking (DST), natural language generation (NLG) and end-to-end (E2E) dialog modeling. In this work, we identify an overlooked issue with dialog state annotation inconsistencies in the dataset, where a slot type is tagged inconsistently across similar dialogs leading to confusion for DST modeling. We propose an automated correction for this issue, which is present in 70% of the dialogs. Additionally, we notice that there is significant entity bias in the dataset (e.g., “cambridge” appears in 50% of the destination cities in the train domain). The entity bias can potentially lead to named entity memorization in generative models, which may go unnoticed as the test set suffers from a similar entity bias as well. We release a new test set with all entities replaced with unseen entities. Finally, we benchmark joint goal accuracy (JGA) of the state-of-the art DST baselines on these modified versions of the data. Our experiments show that the annotation inconsistency corrections lead to 7- 10% improvement in JGA. On the other hand, we observe a 29% drop in JGA when models are evaluated on the new test set with unseen entities.