Search Results for author:
Found 18 papers, 6 papers with code
Investigating the Effect of Background Knowledge on Natural Questions
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.
Automatic Question-Answer Generation for Long-Tail Knowledge
Pretrained Large Language Models (LLMs) have gained significant attention for addressing open-domain Question Answering (QA).
SEMQA: Semi-Extractive Multi-Source Question Answering
Experimenting with several LLMs in various settings, we find this task to be surprisingly challenging, demonstrating the importance of QuoteSum for developing and studying such consolidation capabilities.
Answering Ambiguous Questions with a Database of Questions, Answers, and Revisions
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.
Can Pre-trained Vision and Language Models Answer Visual Information-Seeking Questions?
In this study, we introduce InfoSeek, a visual question answering dataset tailored for information-seeking questions that cannot be answered with only common sense knowledge.
Ranked #3 on
Visual Question Answering (VQA)
on InfoSeek
Reasoning over Logically Interacted Conditions for Question Answering
Even more challenging, we only provide evidences for a subset of the conditions, so some questions may not have deterministic answers.
ConditionalQA: A Complex Reading Comprehension Dataset with Conditional Answers
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.
Adaptable and Interpretable Neural MemoryOver Symbolic Knowledge
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.
Iterative Hierarchical Attention for Answering Complex Questions over Long Documents
We propose a new model, DocHopper, that iteratively attends to different parts of long, hierarchically structured documents to answer complex questions.
Ranked #2 on
Question Answering
on ConditionalQA
Reasoning Over Virtual Knowledge Bases With Open Predicate Relations
We present the Open Predicate Query Language (OPQL); a method for constructing a virtual KB (VKB) trained entirely from text.
Differentiable Open-Ended Commonsense Reasoning
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
Massive language models are the core of modern NLP modeling and have been shown to encode impressive amounts of commonsense and factual information.
Faithful Embeddings for Knowledge Base Queries
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.
Scalable Neural Methods for Reasoning With a Symbolic Knowledge Base
We describe a novel way of representing a symbolic knowledge base (KB) called a sparse-matrix reified KB.
Differentiable Representations For Multihop Inference Rules
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
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 Using Early Fusion of Knowledge Bases and Text
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
Semi-Supervised Learning with Declaratively Specified Entropy Constraints
We propose a technique for declaratively specifying strategies for semi-supervised learning (SSL).
