3.1k citations · 5k across the 42 of their papers we have counts for
14 papers · 1 filter
DynaSent: A Dynamic Benchmark for Sentiment Analysis
Christopher Potts, Zhengxuan Wu, Atticus Geiger +1
We introduce DynaSent ('Dynamic Sentiment'), a new English-language benchmark task for ternary (positive/negative/neutral) sentiment analysis. DynaSent combines naturally occurring…
I like fish, especially dolphins: Addressing Contradictions in Dialogue Modeling
Yixin Nie, Mary Williamson, Mohit Bansal +2
To quantify how well natural language understanding models can capture consistency in a general conversation, we introduce the DialoguE COntradiction DEtection task (DECODE) and a…
Learning from the Worst: Dynamically Generated Datasets to Improve Online Hate Detection
Bertie Vidgen, Tristan Thrush, Zeerak Waseem +1
We present a human-and-model-in-the-loop process for dynamically generating datasets and training better performing and more robust hate detection models. We provide a new dataset…
Reservoir Transformers
Sheng Shen, Alexei Baevski, Ari S. Morcos +3
We demonstrate that transformers obtain impressive performance even when some of the layers are randomly initialized and never updated. Inspired by old and well-established ideas i…
To what extent do human explanations of model behavior align with actual model behavior?
Grusha Prasad, Yixin Nie, Mohit Bansal +3
Given the increasingly prominent role NLP models (will) play in our lives, it is important for human expectations of model behavior to align with actual model behavior. Using Natur…
ANLIzing the Adversarial Natural Language Inference Dataset
Adina Williams, Tristan Thrush, Douwe Kiela
We perform an in-depth error analysis of Adversarial NLI (ANLI), a recently introduced large-scale human-and-model-in-the-loop natural language inference dataset collected over mul…
Exploring Zero-Shot Emergent Communication in Embodied Multi-Agent Populations
Kalesha Bullard, Franziska Meier, Douwe Kiela +2
Effective communication is an important skill for enabling information exchange and cooperation in multi-agent settings. Indeed, emergent communication is now a vibrant field of re…
Learning Optimal Representations with the Decodable Information Bottleneck
Yann Dubois, Douwe Kiela, David J. Schwab +1
We address the question of characterizing and finding optimal representations for supervised learning. Traditionally, this question has been tackled using the Information Bottlenec…
Answering Complex Open-Domain Questions with Multi-Hop Dense Retrieval
Wenhan Xiong, Xiang Lorraine Li, Srini Iyer +8
We propose a simple and efficient multi-hop dense retrieval approach for answering complex open-domain questions, which achieves state-of-the-art performance on two multi-hop datas…
Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks
Patrick Lewis, Ethan Perez, Aleksandra Piktus +9
Large pre-trained language models have been shown to store factual knowledge in their parameters, and achieve state-of-the-art results when fine-tuned on downstream NLP tasks. Howe…
The Hateful Memes Challenge: Detecting Hate Speech in Multimodal Memes
Douwe Kiela, Hamed Firooz, Aravind Mohan +4
This work proposes a new challenge set for multimodal classification, focusing on detecting hate speech in multimodal memes. It is constructed such that unimodal models struggle an…
Multi-Dimensional Gender Bias Classification
Emily Dinan, Angela Fan, Ledell Wu +3
Machine learning models are trained to find patterns in data. NLP models can inadvertently learn socially undesirable patterns when training on gender biased text. In this work, we…
I love your chain mail! Making knights smile in a fantasy game world: Open-domain goal-oriented dialogue agents
Shrimai Prabhumoye, Margaret Li, Jack Urbanek +4
Dialogue research tends to distinguish between chit-chat and goal-oriented tasks. While the former is arguably more naturalistic and has a wider use of language, the latter has cle…
Unsupervised Question Decomposition for Question Answering
Ethan Perez, Patrick Lewis, Wen-tau Yih +2
We aim to improve question answering (QA) by decomposing hard questions into simpler sub-questions that existing QA systems are capable of answering. Since labeling questions with…