Abstract: Literary tropes, from poetry to stories, are at the crux of human imagination and communication. Figurative language, such as a simile, goes beyond plain expressions to give readers new insights and inspirations. We tackle the problem of simile generation. Generating a simile requires proper understanding for effective mapping of properties between two concepts. To this end, we first propose a method to automatically construct a parallel corpus by transforming a large number of similes collected from Reddit to their literal counterpart using structured common sense knowledge. We then fine-tune a pre-trained sequence to sequence model, BART (Lewis et al., 2019), on the literal-simile pairs to generate novel similes given a literal sentence. Experiments show that our approach generates 88% novel similes that do not share properties with the training data. Human evaluation on an independent set of literal statements shows that our model generates similes better than two literary experts 37%1 of the times, and three baseline systems including a recent metaphor generation model 71%2 of the times when compared pairwise.3 We also show how replacing literal sentences with similes from our best model in machine-generated stories improves evocativeness and leads to better acceptance by human judges.
Abstract: Long-form narrative text generated from large language models manages a fluent impersonation of human writing, but only at the local sentence level, and lacks structure or global cohesion. We posit that many of the problems of story generation can be addressed via high-quality content planning, and present a system that focuses on how to learn good plot structures to guide story generation. We utilize a plot-generation language model along with an ensemble of rescoring models that each implement an aspect of good story-writing as detailed in Aristotle’s Poetics. We find that stories written with our more principled plot structure are both more relevant to a given prompt and higher quality than baselines that do not content plan, or that plan in an unprincipled way.
Abstract: In this paper, we propose a neural architecture and a set of training methods for ordering events by predicting temporal relations. Our proposed models receive a pair of events within a span of text as input and they identify temporal relations (Before, After, Equal, Vague) between them. Given that a key challenge with this task is the scarcity of annotated data, our models rely on either pre-trained representations (i.e. RoBERTa, BERT or ELMo), transfer, and multi-task learning (by leveraging complementary datasets), and self-training techniques. Experiments on the MATRES dataset of English documents establish a new state-of-the-art on this task.
Abstract: We study the degree to which neural sequenceto-sequence models exhibit fine-grained controllability when performing natural language generation from a meaning representation. Using two task-oriented dialogue generation benchmarks, we systematically compare the effect of four input linearization strategies on controllability and faithfulness. Additionally, we evaluate how a phrase-based data augmentation method can improve performance. We find that properly aligning input sequences during training leads to highly controllable generation, both when training from scratch or when fine-tuning a larger pre-trained model. Data augmentation further improves control on difficult, randomly generated utterance plans.
Abstract: Stance detection is an important component of understanding hidden influences in everyday life. Since there are thousands of potential topics to take a stance on, most with little to no training data, we focus on zero-shot stance detection: classifying stance from no training examples. In this paper, we present a new dataset for zero-shot stance detection that captures a wider range of topics and lexical variation than in previous datasets. Additionally, we propose a new model for stance detection that implicitly captures relationships between topics using generalized topic representations and show that this model improves performance on a number of challenging linguistic phenomena.
Abstract: We describe a fully unsupervised cross-lingual transfer approach for part-of-speech (POS) tagging under a truly low resource scenario. We assume access to parallel translations between the target language and one or more source languages for which POS taggers are available. We use the Bible as parallel data in our experiments: small size, out-of-domain, and covering many diverse languages. Our approach innovates in three ways: 1) a robust approach of selecting training instances via cross-lingual annotation projection that exploits best practices of unsupervised type and token constraints, word-alignment confidence and density of projected POS, 2) a Bi-LSTM architecture that uses contextualized word embeddings, affix embeddings and hierarchical Brown clusters, and 3) an evaluation on 12 diverse languages in terms of language family and morphological typology. In spite of the use of limited and out-of-domain parallel data, our experiments demonstrate significant improvements in accuracy over previous work. In addition, we show that using multi-source information, either via projection or output combination, improves the performance for most target languages.
Five teams with computer science undergrad and PhD students from the Natural Language Processing Group (NLP) also attended the conference to showcase their work on text summarization, analysis of social media, and fact checking.
”Given the difficult times, we are living in, it’s extremely necessary to be perfect with our facts,” said Tuhin Chakrabarty, lead researcher of the paper. “Misinformation spreads like wildfire and has long-lasting impacts. This motivated us to delve into the area of fact extraction and verification.”
This paper presents the ColumbiaNLP
submission for the FEVER Workshop Shared Task. Their system is an end-to-end pipeline that
extracts factual evidence from Wikipedia and infers a decision about the
truthfulness of the claim based on the extracted evidence.
Fact checking is a type
of investigative journalism where experts examine the claims published by
others for their veracity. The claims can range from statements made by public
figures to stories reported by other publishers. The end goal of a fact
checking system is to provide a verdict on whether the claim is true, false, or
mixed. Several organizations such as FactCheck.org and PolitiFact are devoted
to such activities.
The FEVER Shared task aims to evaluate the ability of a system to verify information using evidence from Wikipedia. Given a claim involving one or more entities (mapping to Wikipedia pages), the system must extract textual evidence (sets of sentences from Wikipedia pages) that supports or refutes the claim and then using this evidence, it must label the claim as Supported, Refuted or NotEnoughInfo.
Detecting Gang-Involved Escalation on Social Media Using Context Serina Chang Computer Science Department, Ruiqi Zhong Computer Science Department, Ethan Adams Computer Science Department, Fei-Tzin Lee Computer Science Department, Siddharth Varia Computer Science Department, Desmond Patton School of Social Work, William Frey School of Social Work, Chris Kedzie Computer Science Department, and Kathleen McKeown Computer Science Department
This research is a
collaboration between Professor Kathy McKeown’s NLP lab and the
Columbia School of Social Work. Professor Desmond Patton, from the School of Social Work and a member of the Data
Science Institute, discovered that gang-involved youth in cities such as
Chicago increasingly turn to social media to grieve the loss of loved ones,
which may escalate into aggression toward rival gangs and plans for violence.
The team created a machine
learning system that can automatically detect aggression and loss in the social
media posts of gang-involved youth. They developed an approach with the hope to
eventually use a system that can save critical time, scale reach, and intervene
before more young lives are lost.
system features the use of word embeddings and lexicons, automatically derived
from a large domain-specific corpus which the team constructed. They also
created context features that capture user’s recent posts, both in semantic and
emotional content, and their interactions with other users in the dataset.
Incorporating domain-specific resources and context feature in a Convolutional
Neural Network (CNN) that leads to a significant improvement over the prior
The dataset used spans the public Twitter posts of nearly 300 users from a gang-involved community in Chicago. Youth volunteers and violence prevention organizations helped identify users and annotate the dataset for aggression and loss. Here are two examples of labeled tweets, both of which the system was able to classify correctly. Names are blocked out to preserve the privacy of users.
For semantics, which were represented by word embeddings, the researchers found that it was optimal to include 90 days of recent tweet history. While for emotion, where an emotion lexicon was employed, only two days of recent tweets were needed. This matched insight from prior social work research, which found that loss is significantly likely to precede aggression in a two-day window. They also found that emotions fluctuate more quickly than semantics so the tighter context window would be able to capture more fine-grained fluctuation.
“We took this context-driven approach because we believed that interpreting emotion in a given tweet requires context, including what the users had been saying recently, how they had been feeling, and their social dynamics with others,” said Serina Chang, an undergraduate computer science student. One thing that surprised them was the extent to which different types of context offered different types of information, as demonstrated by the contrasting contributions of the semantic-based user history feature and the emotion-based one. Continued Chang, “As we hypothesized, adding context did result in a significant performance improvement in our neural net model.”
Automated fact checking of textual claims is of increasing interest in today’s world. Previous research has investigated fact checking in political statements, news articles, and community forums.
“Through our model we can fact check claims
and find specific statements that support the evidence,” said Christopher Hidey,
a fourth year PhD student. “This is a step towards addressing the
propagation of misinformation online.”
As part of the FEVER community
shared task, the researchers developed models that given a statement would jointly find a Wikipedia article and a sentence related
to the statement, and then predict whether the statement is supported by that sentence.
For example, given the claim “Lorelai Gilmore’s father is named Robert,” one could find the Wikipedia article on Lorelai Gilmore and extract the third sentence “Lorelai has a strained relationship with her wealthy parents, Richard and Emily, after running away as a teen to raise her daughter on her own” to show that the claim is false.
One aspect of this problem that the team observed was how poorly TF-IDF, a standard technique in information retrieval and natural language processing, performed at retrieving Wikipedia articles and sentences. Their custom model improved performance by 35 points in terms of recall over a TF-IDF baseline, achieving 90% recall for 5 articles. Overall, the model retrieved the correct sentence and predicted the veracity of the claim 50% of the time.
The rate of which misinformation is spreading on
the web is faster than the rate of manual fact-checking conducted by
organizations like Politifact.com and Factchecking.org. For this paper the
researchers wanted to explore how to automate parts or all of the fact-checking
process. A poster with their findings was presented as part
of the FEVER workshop.
“In order to come up with reliable fact-checking
systems we need to understand the current manual process and identify
opportunities for automation,” said Tariq Alhindi, lead author on the paper. They looked at the LIAR dataset – around 10,000 claims classified by Politifact.com to one of six
degrees of truth – pants-on-fire, false, mostly-false, half-true, mostly-true,
true. Continued Alhindi, we also looked at the fact-checking article for each
claim and automatically extracted justification sentences of a given
verdict and used them in our models, after removing all sentences that contain
the verdict (e.g. true or false).
Feature-based machine learning models and
neural networks were used to develop models that can predict whether
a given statement is true or false. Results showed that using some sort of
justification or evidence always improves the results of fake-news detection
“What was most surprising about the results is that
adding features from the extracted justification sentences consistently improved
the results no matter what classifier we used or what other features we
included,” shared Alhindi, a PhD student. “However, we were surprised that the
improvement was consistent even when we compare
traditional feature-based linear machine learning models against state of
the art deep learning models.”
Their research extends the previous work done on this data set which only looked at the linguistic cues of the claim and/or the metadata of the speaker (history, venue, party-affiliation, etc.). The researchers also released the extended dataset to the community to allow further work on this dataset with the extracted justifications.
a specific type of machine learning, called deep learning, has made strides in
reaching human level performance on hard to articulate problems, that is,
things people do subconsciously like recognizing faces or understanding speech.
And so, natural language processing researchers have turned to these models for
the task of identifying the most important phrases and sentences in text
documents, and have trained them to imitate the decisions a human editor might
make when selecting content for a summary.
learning models have been successful in summarizing natural language texts,
news articles and online comments,” said Chris Kedzie, a fifth
year PhD student. “What we wanted to know is how they are doing it.”
these deep learning models are empirically successful, it is not clear how they
are performing this task. By design, they are learning to create their own
representation of words and sentences, and then using them to predict whether a
sentence is important – if it should go into a summary of the document. But
just what kinds of information are they using to create these
hypotheses the researchers had was that certain types of words were more
informative than others. For example, in a news article, nouns and verbs might
be more important than adjectives and adverbs for identifying the most
important information since such articles are typically written in a relatively
To see if this was so, they trained models to predict sentence importance on redacted datasets, where either nouns, verbs, adjectives, adverbs, or function words were removed and compared them to models trained on the original data.
a dataset of personal stories published on Reddit, adjectives and adverbs were
the key to achieving the best performance. This made intuitive sense in that
people tend to use intensifiers to highlight the most important or climactic
moments in their stories with sentences like, “And those were the WORST
customers I ever served.”
What surprised the researchers were the news articles – removing any one class of words did not dramatically decrease model performance. Either important content was broadly distributed across all kinds of words or there was some other signal that the model was using.
They suspected that sentence order was important because journalists are typically instructed to write according to the inverted pyramid style with the most important information at the top of the article. It was possible that the models were implicitly learning this and simply selecting sentences from the article lead.
Two pieces of evidence confirmed this. First, looking at a histogram of sentence positions selected as important, the models overwhelmingly preferred the lead of the article. Second, in a follow up experiment, the sentence ordered was shuffled to remove sentence position as a viable signal from which to learn. On news articles, model performance dropped significantly, leading to the conclusion that sentence position was most responsible for model performance on news documents.
result concerned the researchers as they want models to be trained to truly
understand human language and not use simple and brittle heuristics (like
sentence position). “To connect this to broader trends in machine learning, we
should be very concerned and careful about what signals are being exploited by
our models, especially when making sensitive decisions,” Kedzie continued. ”The
signals identified by the model as helpful may not truly capture the problem we
are trying to solve, and worse yet, may be exploiting biases in the dataset
that we do not wish it to learn.”
Kedzie sees this as an opportunity to improve the utility of word
representations so that models are better able to use the article content
itself. Along these lines, in the future, he hopes to show that by quantifying
the surprisal or novelty of a particular word or phrase, models are able to
make better sentence importance predictions. Just as people might remember the
most surprising and unexpected parts of a good story.
The Columbia Engineering community has come together to combat the coronavirus pandemic on multiple fronts. In close collabo-ration with the Columbia University Irving Medical Center, we’re leveraging our expertise and innovation to address short term medical needs and long term societal impacts.
Dean Boyce's statement on amicus brief filed by President Bollinger
President Bollinger announced that Columbia University along with many other academic institutions (sixteen, including all Ivy League universities) filed an amicus brief in the U.S. District Court for the Eastern District of New York challenging the Executive Order regarding immigrants from seven designated countries and refugees. Among other things, the brief asserts that “safety and security concerns can be addressed in a manner that is consistent with the values America has always stood for, including the free flow of ideas and people across borders and the welcoming of immigrants to our universities.”
This recent action provides a moment for us to collectively reflect on our community within Columbia Engineering and the importance of our commitment to maintaining an open and welcoming community for all students, faculty, researchers and administrative staff. As a School of Engineering and Applied Science, we are fortunate to attract students and faculty from diverse backgrounds, from across the country, and from around the world. It is a great benefit to be able to gather engineers and scientists of so many different perspectives and talents – all with a commitment to learning, a focus on pushing the frontiers of knowledge and discovery, and with a passion for translating our work to impact humanity.
I am proud of our community, and wish to take this opportunity to reinforce our collective commitment to maintaining an open and collegial environment. We are fortunate to have the privilege to learn from one another, and to study, work, and live together in such a dynamic and vibrant place as Columbia.
Mary C. Boyce
Dean of Engineering
Morris A. and Alma Schapiro Professor