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Found 10 papers, 2 papers with code
Knowledge Informed Semantic Parsing for Conversational Question Answering
Specially, neural semantic parsers (NSPs) effectively translate natural questions to logical forms, which execute on KB and give desirable answers.
Simultaneously Self-Attending to Text and Entities for Knowledge-Informed Text Representations
In this work, we aim at directly learning text representations which leverage structured knowledge about entities mentioned in the text.
Machine Reading Comprehension using Case-based Reasoning
Given a test question, CBR-MRC first retrieves a set of similar cases from a nonparametric memory and then predicts an answer by selecting the span in the test context that is most similar to the contextualized representations of answers in the retrieved cases.
CBR-iKB: A Case-Based Reasoning Approach for Question Answering over Incomplete Knowledge Bases
Yet, in many question answering applications coupled with knowledge bases, the sparse nature of KBs is often overlooked.
TABBIE: Pretrained Representations of Tabular Data
Existing work on tabular representation learning jointly models tables and associated text using self-supervised objective functions derived from pretrained language models such as BERT.
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Case-based Reasoning for Natural Language Queries over Knowledge Bases
It is often challenging to solve a complex problem from scratch, but much easier if we can access other similar problems with their solutions -- a paradigm known as case-based reasoning (CBR).
Knowledge Base Question Answering
Natural Language Queries
+1
Using BibTeX to Automatically Generate Labeled Data for Citation Field Extraction
In this paper, we describe a technique for using BibTeX to generate, automatically, a large-scale 41M labeled strings), labeled dataset, that is four orders of magnitude larger than the current largest CFE dataset, namely the UMass Citation Field Extraction dataset [Anzaroot and McCallum, 2013].
Embedded-State Latent Conditional Random Fields for Sequence Labeling
Complex textual information extraction tasks are often posed as sequence labeling or \emph{shallow parsing}, where fields are extracted using local labels made consistent through probabilistic inference in a graphical model with constrained transitions.
UPS: optimizing Undirected Positive Sparse graph for neural graph filtering
In this work we propose a novel approach for learning graph representation of the data using gradients obtained via backpropagation.
Low-Rank Hidden State Embeddings for Viterbi Sequence Labeling
In textual information extraction and other sequence labeling tasks it is now common to use recurrent neural networks (such as LSTM) to form rich embedded representations of long-term input co-occurrence patterns.
