9 citations · 22 across the 7 of their papers we have counts for
8 papers · 1 filter
Mutual Exclusivity Training and Primitive Augmentation to Induce Compositionality
Yichen Jiang, Xiang Zhou, Mohit Bansal
Recent datasets expose the lack of the systematic generalization ability in standard sequence-to-sequence models. In this work, we analyze this behavior of seq2seq models and ident…
Inducing Transformer's Compositional Generalization Ability via Auxiliary Sequence Prediction Tasks
Yichen Jiang, Mohit Bansal
Systematic compositionality is an essential mechanism in human language, allowing the recombination of known parts to create novel expressions. However, existing neural models have…
Enriching Transformers with Structured Tensor-Product Representations for Abstractive Summarization
Yichen Jiang, Asli Celikyilmaz, Paul Smolensky +7
Abstractive summarization, the task of generating a concise summary of input documents, requires: (1) reasoning over the source document to determine the salient pieces of informat…
HoVer: A Dataset for Many-Hop Fact Extraction And Claim Verification
Yichen Jiang, Shikha Bordia, Zheng Zhong +3
We introduce HoVer (HOppy VERification), a dataset for many-hop evidence extraction and fact verification. It challenges models to extract facts from several Wikipedia articles tha…
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…
Avoiding Reasoning Shortcuts: Adversarial Evaluation, Training, and Model Development for Multi-Hop QA
Yichen Jiang, Mohit Bansal
Multi-hop question answering requires a model to connect multiple pieces of evidence scattered in a long context to answer the question. In this paper, we show that in the multi-ho…