1 citations · 1 across the 3 of their papers we have counts for
6 papers
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…
Ask what's missing and what's useful: Improving Clarification Question Generation using Global Knowledge
Bodhisattwa Prasad Majumder, Sudha Rao, Michel Galley +1
The ability to generate clarification questions i.e., questions that identify useful missing information in a given context, is important in reducing ambiguity. Humans use previous…
Neuro-Symbolic Representations for Video Captioning: A Case for Leveraging Inductive Biases for Vision and Language
Hassan Akbari, Hamid Palangi, Jianwei Yang +6
Neuro-symbolic representations have proved effective in learning structure information in vision and language. In this paper, we propose a new model architecture for learning multi…
Substance over Style: Document-Level Targeted Content Transfer
Allison Hegel, Sudha Rao, Asli Celikyilmaz +1
Existing language models excel at writing from scratch, but many real-world scenarios require rewriting an existing document to fit a set of constraints. Although sentence-level re…
A Recipe for Creating Multimodal Aligned Datasets for Sequential Tasks
Angela S. Lin, Sudha Rao, Asli Celikyilmaz +4
Many high-level procedural tasks can be decomposed into sequences of instructions that vary in their order and choice of tools. In the cooking domain, the web offers many partially…
Generating a Common Question from Multiple Documents using Multi-source Encoder-Decoder Models
Woon Sang Cho, Yizhe Zhang, Sudha Rao +2
Ambiguous user queries in search engines result in the retrieval of documents that often span multiple topics. One potential solution is for the search engine to generate multiple…