Publications

ComplexDataLab at WNUT-2020 Task 2: Detecting Informative COVID-19 Tweets by Attending over Linked Documents
Kellin Pelrine
Jacob Danovitch
Albert Orozco Camacho
Reihaneh Rabbany
Given the global scale of COVID-19 and the flood of social media content related to it, how can we find informative discussions? We present … (voir plus)Gapformer, which effectively classifies content as informative or not. It reformulates the problem as graph classification, drawing on not only the tweet but connected webpages and entities. We leverage a pre-trained language model as well as the connections between nodes to learn a pooled representation for each document network. We show it outperforms several competitive baselines and present ablation studies supporting the benefit of the linked information. Code is available on Github.
2020-10-31
WNUT (publié)
doi.org
Deconstructing word embedding algorithms
Kian Kenyon-Dean
Edward Newell
Jackie Chi Kit Cheung
Word embeddings are reliable feature representations of words used to obtain high quality results for various NLP applications. Uncontextual… (voir plus)ized word embeddings are used in many NLP tasks today, especially in resource-limited settings where high memory capacity and GPUs are not available. Given the historical success of word embeddings in NLP, we propose a retrospective on some of the most well-known word embedding algorithms. In this work, we deconstruct Word2vec, GloVe, and others, into a common form, unveiling some of the common conditions that seem to be required for making performant word embeddings. We believe that the theoretical findings in this paper can provide a basis for more informed development of future models.
2020-10-31
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) (publié)
doi.org
arxiv.org
Experience Grounds Language
Yonatan Bisk
Ari Holtzman
Jesse D. Thomason
Jacob Andreas
Yoshua Bengio
Joyce Yue Chai
Mirella Lapata
Angeliki Lazaridou
Jonathan May
Aleksandr Nisnevich
Nicolas Pinto
Joseph Turian
2020-10-31
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) (publié)
doi.org
arxiv.org
On Extractive and Abstractive Neural Document Summarization with Transformer Language Models
Jonathan Pilault
Raymond Li
Sandeep Subramanian
Christopher Pal
We present a method to produce abstractive summaries of long documents that exceed several thousand words via neural abstractive summarizati… (voir plus)on. We perform a simple extractive step before generating a summary, which is then used to condition the transformer language model on relevant information before being tasked with generating a summary. We show that this extractive step significantly improves summarization results. We also show that this approach produces more abstractive summaries compared to prior work that employs a copy mechanism while still achieving higher rouge scores. Note: The abstract above was not written by the authors, it was generated by one of the models presented in this paper.
2020-10-31
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) (publié)
doi.org
arxiv.org
Factual Error Correction for Abstractive Summarization Models
Meng Cao
Yue Dong
Jiapeng Wu
Jackie Chi Kit Cheung
Neural abstractive summarization systems have achieved promising progress, thanks to the availability of large-scale datasets and models pre… (voir plus)-trained with self-supervised methods. However, ensuring the factual consistency of the generated summaries for abstractive summarization systems is a challenge. We propose a post-editing corrector module to address this issue by identifying and correcting factual errors in generated summaries. The neural corrector model is pre-trained on artificial examples that are created by applying a series of heuristic transformations on reference summaries. These transformations are inspired by an error analysis of state-of-the-art summarization model outputs. Experimental results show that our model is able to correct factual errors in summaries generated by other neural summarization models and outperforms previous models on factual consistency evaluation on the CNN/DailyMail dataset. We also find that transferring from artificial error correction to downstream settings is still very challenging.
2020-10-31
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) (publié)
doi.org
arxiv.org
Inference and Prediction Diverge in Biomedicine
Danilo Bzdok
Denis Engemann
Bertrand Thirion
In the 20th century, many advances in biological knowledge and evidence-based medicine were supported by p values and accompanying methods. … (voir plus)In the early 21st century, ambitions toward precision medicine place a premium on detailed predictions for single individuals. The shift causes tension between traditional regression methods used to infer statistically significant group differences and burgeoning predictive analysis tools suited to forecast an individual's future. Our comparison applies linear models for identifying significant contributing variables and for finding the most predictive variable sets. In systematic data simulations and common medical datasets, we explored how variables identified as significantly relevant and variables identified as predictively relevant can agree or diverge. Across analysis scenarios, even small predictive performances typically coincided with finding underlying significant statistical relationships, but not vice versa. More complete understanding of different ways to define “important” associations is a prerequisite for reproducible research and advances toward personalizing medical care.
2020-10-31
Patterns (publié)
doi.org
Multi-Fact Correction in Abstractive Text Summarization
Yue Dong
Shuohang Wang
Zhe Gan
Yu Cheng
Jackie CK Cheung
Jingjing Liu
2020-10-31
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) (publié)
doi.org
arxiv.org
Multi-XScience: A Large-scale Dataset for Extreme Multi-document Summarization of Scientific Articles
Yao Lu
Yue Dong
Laurent Charlin
Multi-document summarization is a challenging task for which there exists little large-scale datasets. We propose Multi-XScience, a large-sc… (voir plus)ale multi-document summarization dataset created from scientific articles. Multi-XScience introduces a challenging multi-document summarization task: writing the related-work section of a paper based on its abstract and the articles it references. Our work is inspired by extreme summarization, a dataset construction protocol that favours abstractive modeling approaches. Descriptive statistics and empirical results—using several state-of-the-art models trained on the Multi-XScience dataset—reveal that Multi-XScience is well suited for abstractive models.
2020-10-31
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) (publié)
doi.org
arxiv.org
Recursive Top-Down Production for Sentence Generation with Latent Trees
Shawn Tan
Yikang Shen
Timothy J. O'Donnell
Alessandro Sordoni
Aaron Courville
We model the recursive production property of context-free grammars for natural and synthetic languages. To this end, we present a dynamic p… (voir plus)rogramming algorithm that marginalises over latent binary tree structures with
2020-10-31
Findings of the Association for Computational Linguistics: EMNLP 2020 (publié)
doi.org
arxiv.org
Supervised Seeded Iterated Learning for Interactive Language Learning
Yuchen Lu
Soumye Singhal
Florian Strub
Olivier Pietquin
Aaron Courville
2020-10-31
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) (publié)
doi.org
arxiv.org
TeMP: Temporal Message Passing for Temporal Knowledge Graph Completion
Jiapeng Wu
Meng Cao
Jackie Chi Kit Cheung
William L. Hamilton
Inferring missing facts in temporal knowledge graphs (TKGs) is a fundamental and challenging task. Previous works have approached this probl… (voir plus)em by augmenting methods for static knowledge graphs to leverage time-dependent representations. However, these methods do not explicitly leverage multi-hop structural information and temporal facts from recent time steps to enhance their predictions. Additionally, prior work does not explicitly address the temporal sparsity and variability of entity distributions in TKGs. We propose the Temporal Message Passing (TeMP) framework to address these challenges by combining graph neural networks, temporal dynamics models, data imputation and frequency-based gating techniques. Experiments on standard TKG tasks show that our approach provides substantial gains compared to the previous state of the art, achieving a 10.7% average relative improvement in Hits@10 across three standard benchmarks. Our analysis also reveals important sources of variability both within and across TKG datasets, and we introduce several simple but strong baselines that outperform the prior state of the art in certain settings.
2020-10-31
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) (publié)
doi.org
arxiv.org
TESA: A Task in Entity Semantic Aggregation for Abstractive Summarization
Clement Jumel
Annie Priyadarshini Louis
Jackie CK Cheung
Human-written texts contain frequent generalizations and semantic aggregation of content. In a document, they may refer to a pair of named e… (voir plus)ntities such as ‘London’ and ‘Paris’ with different expressions: “the major cities”, “the capital cities” and “two European cities”. Yet generation, especially, abstractive summarization systems have so far focused heavily on paraphrasing and simplifying the source content, to the exclusion of such semantic abstraction capabilities. In this paper, we present a new dataset and task aimed at the semantic aggregation of entities. TESA contains a dataset of 5.3K crowd-sourced entity aggregations of Person, Organization, and Location named entities. The aggregations are document-appropriate, meaning that they are produced by annotators to match the situational context of a given news article from the New York Times. We then build baseline models for generating aggregations given a tuple of entities and document context. We finetune on TESA an encoder-decoder language model and compare it with simpler classification methods based on linguistically informed features. Our quantitative and qualitative evaluations show reasonable performance in making a choice from a given list of expressions, but free-form expressions are understandably harder to generate and evaluate.
2020-10-31
Conference on Empirical Methods in Natural Language Processing (publié)
doi.org