153 citations · 601 across the 67 of their papers we have counts for
18 papers · 1 filter
Self-Assembling Modular Networks for Interpretable Multi-Hop Reasoning
Yichen Jiang, Mohit Bansal
Multi-hop QA requires a model to connect multiple pieces of evidence scattered in a long context to answer the question. The recently proposed HotpotQA (Yang et al., 2018) dataset…
Adversarial NLI: A New Benchmark for Natural Language Understanding
Yixin Nie, Adina Williams, Emily Dinan +3
We introduce a new large-scale NLI benchmark dataset, collected via an iterative, adversarial human-and-model-in-the-loop procedure. We show that training models on this new datase…
Automatically Learning Data Augmentation Policies for Dialogue Tasks
Tong Niu, Mohit Bansal
Automatic data augmentation (AutoAugment) (Cubuk et al., 2019) searches for optimal perturbation policies via a controller trained using performance rewards of a sampled policy on…
Revealing the Importance of Semantic Retrieval for Machine Reading at Scale
Yixin Nie, Songhe Wang, Mohit Bansal
Machine Reading at Scale (MRS) is a challenging task in which a system is given an input query and is asked to produce a precise output by "reading" information from a large knowle…
Addressing Semantic Drift in Question Generation for Semi-Supervised Question Answering
Shiyue Zhang, Mohit Bansal
Text-based Question Generation (QG) aims at generating natural and relevant questions that can be answered by a given answer in some context. Existing QG models suffer from a "sema…
LXMERT: Learning Cross-Modality Encoder Representations from Transformers
Hao Tan, Mohit Bansal
Vision-and-language reasoning requires an understanding of visual concepts, language semantics, and, most importantly, the alignment and relationships between these two modalities.…