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Linguistic Regularities in Continuous Space Word Representations
TLDR
The vector-space word representations that are implicitly learned by the input-layer weights are found to be surprisingly good at capturing syntactic and semantic regularities in language, and that each relationship is characterized by a relation-specific vector offset. Expand
Embedding Entities and Relations for Learning and Inference in Knowledge Bases
TLDR
It is found that embeddings learned from the bilinear objective are particularly good at capturing relational semantics and that the composition of relations is characterized by matrix multiplication. Expand
Dense Passage Retrieval for Open-Domain Question Answering
TLDR
This work shows that retrieval can be practically implemented using dense representations alone, where embeddings are learned from a small number of questions and passages by a simple dual-encoder framework. Expand
WikiQA: A Challenge Dataset for Open-Domain Question Answering
TLDR
The WIKIQA dataset is described, a new publicly available set of question and sentence pairs, collected and annotated for research on open-domain question answering, which is more than an order of magnitude larger than the previous dataset. Expand
QuAC: Question Answering in Context
TLDR
QuAC introduces challenges not found in existing machine comprehension datasets: its questions are often more open-ended, unanswerable, or only meaningful within the dialog context, as it shows in a detailed qualitative evaluation. Expand
Semantic Parsing via Staged Query Graph Generation: Question Answering with Knowledge Base
TLDR
This work proposes a novel semantic parsing framework for question answering using a knowledge base that leverages the knowledge base in an early stage to prune the search space and thus simplifies the semantic matching problem. Expand
Dissecting Contextual Word Embeddings: Architecture and Representation
TLDR
There is a tradeoff between speed and accuracy, but all architectures learn high quality contextual representations that outperform word embeddings for four challenging NLP tasks, suggesting that unsupervised biLMs, independent of architecture, are learning much more about the structure of language than previously appreciated. Expand
A Knowledge-Grounded Neural Conversation Model
TLDR
A novel, fully data-driven, and knowledge-grounded neural conversation model aimed at producing more contentful responses that generalizes the widely-used Sequence-to-Sequence (seq2seq) approach by conditioning responses on both conversation history and external “facts”, allowing the model to be versatile and applicable in an open-domain setting. Expand
The Importance of Syntactic Parsing and Inference in Semantic Role Labeling
TLDR
It is shown that full syntactic parsing information is, by far, most relevant in identifying the argument, especially in the very first stagethe pruning stage, and an effective and simple approach of combining different semantic role labeling systems through joint inference is proposed, which significantly improves its performance. Expand
Cross-Sentence N-ary Relation Extraction with Graph LSTMs
TLDR
A general relation extraction framework based on graph long short-term memory networks (graph LSTMs) that can be easily extended to cross-sentence n-ary relation extraction is explored, demonstrating its effectiveness with both conventional supervised learning and distant supervision. Expand
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