Natural Language Processing Papers Accepted to EMNLP 2020


Six papers from the Speech & NLP group were accepted to the Empirical Methods in Natural Language Processing (EMNLP) conference. 

Generating Similes Effortlessly Like a Pro: A Style Transfer Approach for Simile Generation
Tuhin Chakrabarty Columbia University, Smaranda Muresan Columbia University, and Nanyun Peng University of Southern California and University of California, Los Angeles

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.


Content Planning for Neural Story Generation with Aristotelian Rescoring
Seraphina Goldfarb-Tarrant University of Southern California and University of Edinburgh, Tuhin Chakrabarty Columbia University, Ralph Weischedel University of Southern California and Nanyun Peng University of Southern California and University of California, Los Angeles

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.


Severing the Edge Between Before and After: Neural Architectures for Temporal Ordering of Events
Miguel Ballesteros Amazon AI, Rishita Anubhai Amazon AI, Shuai Wang Amazon AI, Nima Pourdamghani Amazon AI, Yogarshi Vyas Amazon AI, Jie Ma Amazon AI, Parminder Bhatia Amazon AI, Kathleen McKeown Columbia University and Amazon AI and Yaser Al-Onaizan Amazon AI

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.


Controllable Meaning Representation to Text Generation: Linearization and Data Augmentation Strategies
Chris Kedzie Columbia University and Kathleen McKeown Columbia University

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.

Zero-Shot Stance Detection: A Dataset and Model using Generalized Topic Representations
Emily Allaway Columbia University and Kathleen McKeown Columbia University

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.


Unsupervised Cross-Lingual Part-of-Speech Tagging for Truly Low-Resource Scenarios
Ramy Eskander Columbia University, Smaranda Muresan Columbia University, and Michael Collins Columbia University

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.