Search Results for author: Haitian Sun

Found 16 papers, 5 papers with code

Investigating the Effect of Background Knowledge on Natural Questions

no code implementations NAACL (DeeLIO) 2021 Vidhisha Balachandran, Bhuwan Dhingra, Haitian Sun, Michael Collins, William Cohen

We create a subset of the NQ data, Factual Questions (FQ), where the questions have evidence in the KB in the form of paths that link question entities to answer entities but still must be answered using text, to facilitate further research into KB integration methods.

Natural Questions Retrieval

Answering Ambiguous Questions with a Database of Questions, Answers, and Revisions

no code implementations16 Aug 2023 Haitian Sun, William W. Cohen, Ruslan Salakhutdinov

Many open-domain questions are under-specified and thus have multiple possible answers, each of which is correct under a different interpretation of the question.

Passage Retrieval Question Answering +1

Can Pre-trained Vision and Language Models Answer Visual Information-Seeking Questions?

2 code implementations23 Feb 2023 Yang Chen, Hexiang Hu, Yi Luan, Haitian Sun, Soravit Changpinyo, Alan Ritter, Ming-Wei Chang

Our analysis shows that it is challenging for the state-of-the-art multi-modal pre-trained models to answer visual information seeking questions, but this capability is improved through fine-tuning on the automated InfoSeek dataset.

Open-Domain Question Answering Visual Question Answering

Reasoning over Logically Interacted Conditions for Question Answering

no code implementations25 May 2022 Haitian Sun, William W. Cohen, Ruslan Salakhutdinov

Even more challenging, we only provide evidences for a subset of the conditions, so some questions may not have deterministic answers.

Logical Reasoning Question Answering

ConditionalQA: A Complex Reading Comprehension Dataset with Conditional Answers

2 code implementations ACL 2022 Haitian Sun, William W. Cohen, Ruslan Salakhutdinov

In addition to conditional answers, the dataset also features: (1) long context documents with information that is related in logically complex ways; (2) multi-hop questions that require compositional logical reasoning; (3) a combination of extractive questions, yes/no questions, questions with multiple answers, and not-answerable questions; (4) questions asked without knowing the answers.

Logical Reasoning Question Answering +1

Iterative Hierarchical Attention for Answering Complex Questions over Long Documents

no code implementations1 Jun 2021 Haitian Sun, William W. Cohen, Ruslan Salakhutdinov

We propose a new model, DocHopper, that iteratively attends to different parts of long, hierarchically structured documents to answer complex questions.

Multi-hop Question Answering Question Answering +1

Adaptable and Interpretable Neural MemoryOver Symbolic Knowledge

no code implementations NAACL 2021 Pat Verga, Haitian Sun, Livio Baldini Soares, William Cohen

Past research has demonstrated that large neural language models (LMs) encode surprising amounts of factual information: however, augmenting or modifying this information requires modifying a corpus and retraining, which is computationally expensive.

Question Answering

Reasoning Over Virtual Knowledge Bases With Open Predicate Relations

no code implementations14 Feb 2021 Haitian Sun, Pat Verga, Bhuwan Dhingra, Ruslan Salakhutdinov, William W. Cohen

We present the Open Predicate Query Language (OPQL); a method for constructing a virtual KB (VKB) trained entirely from text.

Language Modelling Open-Domain Question Answering

Differentiable Open-Ended Commonsense Reasoning

no code implementations NAACL 2021 Bill Yuchen Lin, Haitian Sun, Bhuwan Dhingra, Manzil Zaheer, Xiang Ren, William W. Cohen

As a step towards making commonsense reasoning research more realistic, we propose to study open-ended commonsense reasoning (OpenCSR) -- the task of answering a commonsense question without any pre-defined choices -- using as a resource only a corpus of commonsense facts written in natural language.


Facts as Experts: Adaptable and Interpretable Neural Memory over Symbolic Knowledge

no code implementations2 Jul 2020 Pat Verga, Haitian Sun, Livio Baldini Soares, William W. Cohen

Massive language models are the core of modern NLP modeling and have been shown to encode impressive amounts of commonsense and factual information.

Language Modelling Question Answering

Faithful Embeddings for Knowledge Base Queries

1 code implementation NeurIPS 2020 Haitian Sun, Andrew O. Arnold, Tania Bedrax-Weiss, Fernando Pereira, William W. Cohen

We address this problem with a novel QE method that is more faithful to deductive reasoning, and show that this leads to better performance on complex queries to incomplete KBs.

Question Answering

Scalable Neural Methods for Reasoning With a Symbolic Knowledge Base

1 code implementation ICLR 2020 William W. Cohen, Haitian Sun, R. Alex Hofer, Matthew Siegler

We describe a novel way of representing a symbolic knowledge base (KB) called a sparse-matrix reified KB.

Differentiable Representations For Multihop Inference Rules

no code implementations24 May 2019 William W. Cohen, Haitian Sun, R. Alex Hofer, Matthew Siegler

We present efficient differentiable implementations of second-order multi-hop reasoning using a large symbolic knowledge base (KB).

PullNet: Open Domain Question Answering with Iterative Retrieval on Knowledge Bases and Text

no code implementations IJCNLP 2019 Haitian Sun, Tania Bedrax-Weiss, William W. Cohen

We focus on a setting in which a corpus is supplemented with a large but incomplete KB, and on questions that require non-trivial (e. g., ``multi-hop'') reasoning.

Open-Domain Question Answering Retrieval

Open Domain Question Answering Using Early Fusion of Knowledge Bases and Text

2 code implementations EMNLP 2018 Haitian Sun, Bhuwan Dhingra, Manzil Zaheer, Kathryn Mazaitis, Ruslan Salakhutdinov, William W. Cohen

In this paper we look at a more practical setting, namely QA over the combination of a KB and entity-linked text, which is appropriate when an incomplete KB is available with a large text corpus.

Graph Representation Learning Open-Domain Question Answering

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