Answer Extraction as Sequence Tagging with Tree Edit Distance

By Xuchen Yao, Benjamin Van Durme, Chris Callison-Burch, Peter Clark. 2013

217 citations recorded by the source corpus.

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In Natural Language Processing

Citations in the corpus

Data Augmentation for BERT Fine-Tuning in Open-Domain Question Answering (2019, background): “This stands in contrast to reading comprehension datasets such as SQuAD (Rajpurkar et al., 2016), where the system works with a single pre-determined document, or most QA benchmarks today such as TrecQA (Yao et al., 2013), WikiQA (Yang et al., 2015), and MSMARCO (Bajaj et al., 2016), where the syste”
End-to-End Open-Domain Question Answering with BERTserini (2019, background): “Most popular QA benchmark datasets today—for example, TrecQA (Yao et al., 2013), WikiQA (Yang et al., 2015), and MSMARCO (Bajaj et al., 2016)—are best characterized as answer selection tasks.”
WikiQA: A Challenge Dataset for Open-Domain Question Answering (2015, background): “Answer sentence selection is a crucial subtask of the open-domain question answering (QA) problem, with the goal of extracting answers from a set of pre-selected sentences (Heilman and Smith, 2010; Yao et al., 2013; Severyn and Moschitti, 2013).”
Deep Learning for Answer Sentence Selection (2014, positive): “[21] extended Heilman and Smith’s approach with the difference that they used dyna mic programming to find the optimal tree edit sequences.”
Attentive Pooling Networks (2016, positive): “git The data is obtained from (Yao et al., 2013)”
Simple Applications of BERT for Ad Hoc Document Retrieval (2019, methodology): “Speciﬁcally, we used: (1) the microblog data from the previous section and (2) the union of the TrecQA (Yao et al., 2013) and WikiQA (Yang et al., 2015) datasets.”
Pairwise Word Interaction Modeling with Deep Neural Networks for Semantic Similarity Measurement (2016, methodology): “Previous approaches with probabilistic tree-edit techniques or tree kernels (Wang and Manning, 2010; Heilman and Smith, 2010; Yao et al., 2013) have been successful since tree structure information perModel MAP MRR”
A Long Short-Term Memory Model for Answer Sentence Selection in Question Answering (2015, methodology): “Later, tree kernel function together with a logistic regression model (Heilman and Smith, 2010) or Conditional Random Fields models (Wang and Manning, 2010; Yao et al., 2013) with extracted feature were adopted to learn the associations between question and answer.”
A Survey on Text Classification: From Traditional to Deep Learning (2020, unknown): “F 5 93 3,650,000 English [93, 108, 122] [232] SA Twitter 3 19 11,209 English [5][100] [233] SA NLP&CC 2013 2 - 115,606 Multi-language [110] [111] SA 20NG 20 221 18,846 English [72, 75, 106, 155, 157] [34] NC AG News 4 45/7 127,600 English [98, 100] [101, 138] [234] NC R8 8 66 7,674 English [155, 157”
Modelling input texts: from Tree Kernels to Deep Learning (2015, methodology): “Unlike the CRF model which obtains higher values of precision, our system acts as a high recall system able to recover most of the answers from the correct 15We could not replicate the results obtained in [186] with the forced voting strategy.”
LSTM-based Deep Learning Models for non-factoid answer selection (2015, methodology): “Our LSTM implementation is similar to the one in (Graves et al., 2013) with minor 2The data is obtained from (Yao et al., 2013) http://cs.jhu.edu/˜xuchen/packages/ jacana-qa-naacl2013-data-results.tar.bz2 modification.”
Question Answering on Freebase via Relation Extraction and Textual Evidence (2016, background): “TREC QA evaluations (Voorhees and Tice, 1999) were a major boost to unstructured QA leading to richer datasets and sophisticated methods (Wang et al., 2007; Heilman and Smith, 2010; Yao et al., 2013; Yih et al., 2013; Yu et al., 2014; Yang et al., 2015; Hermann et al., 2015).”
Statute Law Information Retrieval and Entailment (2019, background): “Extensive research has explored employing syntactic/semantic features [4, 5] and recently using neural networks [6].”
Convolutional Neural Networks vs. Convolution Kernels: Feature Engineering for Answer Sentence Reranking (2016, unknown): “(Yao et al., 2013) applied linear chain CRFs with features derived from TED to automatically learn associations between questions and candidate answers.”
Transforming Dependency Structures to Logical Forms for Semantic Parsing (2016, methodology): “…without going through an intermediate semantic representation, e.g., answer-sentence selection (Punyakanok et al., 2004; Heilman and Smith, 2010; Yao et al., 2013) and semantic parsing (Ge and Mooney, 2009; Poon, 2013; Parikh et al., 2015; Xu et al., 2015; Wang et al., 2015; Andreas and Klein,…”
Inner Attention based Recurrent Neural Networks for Answer Selection (2016, background): “Given a question, the goal is to choose the answer from a set of pre-selected sentences (Heilman and Smith, 2010; Yao et al., 2013).”
Discriminative Information Retrieval for Question Answering Sentence Selection (2017, background): “Extensive research has explored employing syntactic/semantic features (Yih et al., 2013; Wang and Manning, 2010; Heilman and Smith, 2010; Yao et al., 2013a) and recently using neural networks (Yu et al., 2014; Severyn and Mos-chitti, 2015; Wang and Nyberg, 2015; Yin et al., 2016).”