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Multi-View Learning for Vision-and-Language Navigation
Qiaolin Xia, Xiujun Li, Chunyuan Li, Yonatan Bisk, Zhifang Sui, Yejin Choi, Noah A. SmitharXiv • 2020 Learning to navigate in a visual environment following natural language instructions is a challenging task because natural language instructions are highly variable, ambiguous, and under-specified. In this paper, we present a novel training paradigm, Learn…Fine-Tuning Pretrained Language Models: Weight Initializations, Data Orders, and Early Stopping
Jesse Dodge, Gabriel Ilharco, Roy Schwartz, Ali Farhadi, Hannaneh Hajishirzi, Noah A. Smith arXiv • 2020 Fine-tuning pretrained contextual word embedding models to supervised downstream tasks has become commonplace in natural language processing. This process, however, is often brittle: even with the same hyperparameter values, distinct random seeds can lead to…WinoGrande: An Adversarial Winograd Schema Challenge at Scale
Keisuke Sakaguchi, Ronan Le Bras, Chandra Bhagavatula, Yejin ChoiAAAI • 2020 The Winograd Schema Challenge (WSC), proposed by Levesque et al. (2011) as an alternative to the Turing Test, was originally designed as a pronoun resolution problem that cannot be solved based on statistical patterns in large text corpora. However, recent…PIQA: Reasoning about Physical Commonsense in Natural Language
Yonatan Bisk, Rowan Zellers, Ronan Le Bras, Jianfeng Gao, Yejin ChoiAAAI • 2020 To apply eyeshadow without a brush, should I use a cotton swab or a toothpick? Questions requiring this kind of physical commonsense pose a challenge to today's natural language understanding systems. While recent pretrained models (such as BERT) have made…Probing Natural Language Inference Models through Semantic Fragments
Kyle Richardson, Hai Na Hu, Lawrence S. Moss, Ashish SabharwalAAAI • 2020 Do state-of-the-art models for language understanding already have, or can they easily learn, abilities such as boolean coordination, quantification, conditionals, comparatives, and monotonicity reasoning (i.e., reasoning about word substitutions in…MonaLog: a Lightweight System for Natural Language Inference Based on Monotonicity
Hai Hu, Qi Chen, Kyle Richardson, Atreyee Mukherjee, Lawrence S. Moss, Sandra Kübler SCIL • 2020 We present a new logic-based inference engine for natural language inference (NLI) called MonaLog, which is based on natural logic and the monotonicity calculus. In contrast to existing logic-based approaches, our system is intentionally designed to be as…Commonsense Knowledge Base Completion with Structural and Semantic Context
Chaitanya Malaviya, Chandra Bhagavatula, Antoine Bosselut, Yejin ChoiAAAI • 2019 Automatic KB completion for commonsense knowledge graphs (e.g., ATOMIC and ConceptNet) poses unique challenges compared to the much studied conventional knowledge bases (e.g., Freebase). Commonsense knowledge graphs use free-form text to represent nodes…Just Add Functions: A Neural-Symbolic Language Model
David Demeter, Doug DowneyarXiv • 2019 Neural network language models (NNLMs) have achieved ever-improving accuracy due to more sophisticated architectures and increasing amounts of training data. However, the inductive bias of these models (formed by the distributional hypothesis of language…Analyzing Compositionality in Visual Question Answering
Sanjay Subramanian, Sameer Singh, Matt GardnerNeurIPS • ViGIL Workshop • 2019 Since the release of the original Visual Question Answering (VQA) dataset, several newer datasets for visual reasoning have been introduced, often with the express intent of requiring systems to perform compositional reasoning. Recently, transformer models…Defending Against Neural Fake News
Rowan Zellers, Ari Holtzman, Hannah Rashkin, Yonatan Bisk, Ali Farhadi, Franziska Roesner, Yejin ChoiNeurIPS • 2019 Recent progress in natural language generation has raised dual-use concerns. While applications like summarization and translation are positive, the underlying technology also might enable adversaries to generate neural fake news: targeted propaganda that…