Search Results for author: Yichen Jiang

Found 10 papers, 6 papers with code

Inducing Transformer’s Compositional Generalization Ability via Auxiliary Sequence Prediction Tasks

1 code implementation EMNLP 2021 Yichen Jiang, Mohit Bansal

Motivated by the failure of a Transformer model on the SCAN compositionality challenge (Lake and Baroni, 2018), which requires parsing a command into actions, we propose two auxiliary sequence prediction tasks as additional training supervision.

Learning and Analyzing Generation Order for Undirected Sequence Models

no code implementations Findings (EMNLP) 2021 Yichen Jiang, Mohit Bansal

In this work, we train a policy that learns the generation order for a pre-trained, undirected translation model via reinforcement learning.

Machine Translation Translation

Inducing Transformer's Compositional Generalization Ability via Auxiliary Sequence Prediction Tasks

1 code implementation30 Sep 2021 Yichen Jiang, Mohit Bansal

Motivated by the failure of a Transformer model on the SCAN compositionality challenge (Lake and Baroni, 2018), which requires parsing a command into actions, we propose two auxiliary sequence prediction tasks that track the progress of function and argument semantics, as additional training supervision.

Enriching Transformers with Structured Tensor-Product Representations for Abstractive Summarization

1 code implementation NAACL 2021 Yichen Jiang, Asli Celikyilmaz, Paul Smolensky, Paul Soulos, Sudha Rao, Hamid Palangi, Roland Fernandez, Caitlin Smith, Mohit Bansal, Jianfeng Gao

On several syntactic and semantic probing tasks, we demonstrate the emergent structural information in the role vectors and improved syntactic interpretability in the TPR layer outputs.

Abstractive Text Summarization

Self-Assembling Modular Networks for Interpretable Multi-Hop Reasoning

1 code implementation IJCNLP 2019 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.

Explore, Propose, and Assemble: An Interpretable Model for Multi-Hop Reading Comprehension

1 code implementation ACL 2019 Yichen Jiang, Nitish Joshi, Yen-Chun Chen, Mohit Bansal

Multi-hop reading comprehension requires the model to explore and connect relevant information from multiple sentences/documents in order to answer the question about the context.

Multi-Hop Reading Comprehension

Closed-Book Training to Improve Summarization Encoder Memory

no code implementations EMNLP 2018 Yichen Jiang, Mohit Bansal

A good neural sequence-to-sequence summarization model should have a strong encoder that can distill and memorize the important information from long input texts so that the decoder can generate salient summaries based on the encoder's memory.

Abstractive Text Summarization

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