More Accurate Question Answering on Freebase

By Hannah Bast, Elmar Haussmann. 2015

263 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Natural Language Processing

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Citations in the corpus

Evidence Aggregation for Answer Re-Ranking in Open-Domain Question Answering (2017, background): “(2) KB-QA systems (Bast & Haussmann, 2015; Yih et al., 2015; Xu et al., 2016) sometimes use text evidence to enhance answer re-ranking, where the features are also extracted on the pair of question and a single-passage but ignored the union information among multiple passages.”
The Value of Semantic Parse Labeling for Knowledge Base Question Answering (2016, methodology): “The design of our UI is inspired by recent work on semantic parsing that has been applied to the W EB Q UESTIONS dataset (Bast and Haussmann, 2015; Reddy et al., 2014; Berant and Liang, 2014; Yih et al., 2015), as these approaches use a simpler and yet more restricted semantic representation than…”
Improved Neural Relation Detection for Knowledge Base Question Answering (2017, background): “tion detection for KBQA also starts with featurerich approaches (Yao and Van Durme, 2014; Bast and Haussmann, 2015) towards usages of deep networks (Yih et al.”
LEGO: Latent Execution-Guided Reasoning for Multi-Hop Question Answering on Knowledge Graphs (2021, methodology): “Traditional KGQA methods (Berant et al., 2013; Bast & Haussmann, 2015) use hand-engineered templates to parse the question and synthesize the query (Poon & Domin-gos, 2009).”
PullNet: Open Domain Question Answering with Iterative Retrieval on Knowledge Bases and Text (2019, background): “Another line of QA work from text and KBs is exemplified by AQQU (Bast and Haussmann, 2015) and its successors (Sawant et al.”
Sequence-to-Action: End-to-End Semantic Graph Generation for Semantic Parsing (2018, methodology): “Semantic graph-based methods (Reddy et al., 2014, 2016; Bast and Haussmann, 2015; Yih et al., 2015) represent the meaning of a sentence as a semantic graph (i.”
Learning an Executable Neural Semantic Parser (2017, unknown): “It is important to note that Bast and Haussmann (2015) develop a question-answering system, which, contrary to ours, cannot produce meaning representations, whereas Berant and Liang (2015) propose a sophisticated agenda-based parser that is trained borrowing ideas from imitation learning.”
Question Answering on Freebase via Relation Extraction and Textual Evidence (2016, methodology): “On the other hand, instead of building a formal meaning representation, information extraction methods retrieve a set of candidate answers from KB using relation extraction (Yao and Van Durme, 2014; Yih et al., 2014; Yao, 2015; Bast and Haussmann, 2015) or distributed representations (Bordes et al.”
Core techniques of question answering systems over knowledge bases: a survey (2017, unknown): “This is for example the case for Bor-des et al. 2015, Yang et al. 2014, Yavuz et al. 2016, STAGG (Yih et al. 2015) (Bast & Haussmann 2015) or Reddy et al. 2016.”
Transforming Dependency Structures to Logical Forms for Semantic Parsing (2016, methodology): “0 – Berant et al. (2013) 62.”
ReTraCk: A Flexible and Efficient Framework for Knowledge Base Question Answering (2021, methodology): “Com Zero Aqqu (Bast and Haussmann, 2015) 14.5 − − − GrailQA (Test set ) (Gu et al., 2021) 75.2 − − − GrailQA (Dev set) (Gu et al., 2021) 72.2 items get higher scores with the given question.”
ComQA: A Community-sourced Dataset for Complex Factoid Question Answering with Paraphrase Clusters (2018, methodology): “…on natural language questions as an interface for KBs, where questions are translated to structured queries via semantic parsing (Bao et al., 2016; Bast and Haussmann, 2015; Berant et al., 2013; Fader et al., 2013; Reddy et al., 2014; Mohammed et al., 2018; Xu et al., 2016; Yang et al., 2014; Yao…”
Learning Structured Natural Language Representations for Semantic Parsing (2017, methodology): “…against a variety of models previously described Models F1 Berant et al. (2013) 35.7 Yao and Van Durme (2014) 33.0 Berant and Liang (2014) 39.9 Bast and Haussmann (2015) (Berant et al., 2013) 10.80 PARASEMPRE (Berant and Liang, 2014) 12.79 JACANA (Yao and Van Durme, 2014) 5.08 Neural Baseline…”
Neural, symbolic and neural-symbolic reasoning on knowledge graphs (2021, background): “As one of the pioneering work, Bast and Haussmann (2015) manually construct three simple question templates without QA corpora.”
SLiQA-I: Towards Cold-start Development of End-to-end Spoken Language Interface for Question Answering (2019, background): “QA engine: Most recent works [7, 8, 9, 10, 14] are designed based on predefined templates.”