Open question answering over curated and extracted knowledge bases

By Anthony Fader, Luke S. Zettlemoyer, Oren Etzioni. 2014

450 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Retrieval & Search

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Citations in the corpus

TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehension (2017, background): “Other recent approaches attempt to combine structured high precision KBs with semistructured information sources like OpenIE triples (Fader et al., 2014), HTML tables (Pasupat and Liang, 2015), and large (and noisy) corpora (Sawant and Chakrabarti, 2013; Joshi et al.”
Latent Retrieval for Weakly Supervised Open Domain Question Answering (2019, background): “Our approach is also reminiscent of weakly supervised semantic parsing (Clarke et al., 2010; Liang et al., 2013; Artzi and Zettlemoyer, 2013; Fader et al., 2014; Berant et al., 2013; Kwiatkowski et al., 2013), with which we share similar challenges—(1) inference and learning are tightly coupled,…”
Reading Wikipedia to Answer Open-Domain Questions (2017, methodology): “Fader et al. (2014) used We-bQuestions, TREC and WikiAnswers with four KBs as knowledge sources and reported improvement on the latter two datasets through multi-task learning.”
Denoising Distantly Supervised Open-Domain Question Answering (2018, background): “, 2015) or automatically extracted relational triples (Fader et al., 2014).”
Key-Value Memory Networks for Directly Reading Documents (2016, methodology): “, 2008) have led to the development of a new class of QA methods based on semantic parsing (Berant et al., 2013; Kwiatkowski et al., 2013; Fader et al., 2014; Yih et al., 2015) that can return precise answers to complicated compositional questions.”
Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks (2015, background): “We show our proposed approach does indeed give improved performance on some tasks, but is still unable to solve some of them, which we consider as open problems.”
VQA: Visual Question Answering (2015, background): “Text-based Q&A is a well studied problem in the NLP and text processing communities (recent examples being [12, 11, 46, 38]).”
Compositional Semantic Parsing on Semi-Structured Tables (2015, background): “More recent work sacrifices compositionality in favor of using more open-ended knowledge bases such as Freebase (Cai and Yates, 2013; Berant et al., 2013; Fader et al., 2014; Reddy et al., 2014).”
A dataset for Movie Description (2015, background): “Semantic parsing has received much attention in computational linguistics recently, see, for example, the tutorial [6] and references given there.”
Yin and Yang: Balancing and Answering Binary Visual Questions (2015, background): “Text-based QA is a well studied problem in the natural language processing (NLP) community, recent examples being [14, 15, 33, 40].”
Evaluating Prerequisite Qualities for Learning End-to-End Dialog Systems (2015, background): “There has been a recent surge in interest in such systems that try to answer a question posed in natural language by converting it into a database search over a knowledge base (Berant & Liang, 2014; Kwiatkowski et al., 2013; Fader et al., 2014), which is a setup natural for our QA task also.”
Combining Retrieval, Statistics, and Inference to Answer Elementary Science Questions (2016, background): “…to short, factoid questions (e.g., “In which year was Bill Clinton born?”) by locating answers in databases (Yao and Van Durme 2014; Zou et al. 2014; Fader, Zettlemoyer, and Etzioni 2014) or large document collections (Brill, Dumais, and Banko 2002; Ferrucci et al. 2010; Ko, Nyberg, and Si 2007).”
Search-based Neural Structured Learning for Sequential Question Answering (2017, background): “75% questions with more than 15 words in WikiAnswers (Fader et al., 2014).”
Explicit Knowledge-based Reasoning for Visual Question Answering (2015, methodology): “ailable. The advances in structured KBs, have driven an increasing interest in the NLP and AI communities in the problem of natural language question answering using structured KBs (refer to as KBQA) [4, 7, 8, 16, 23, 25, 41, 26, 38]. The VQA approach which is closest to KB-QA (and to our approach) ”
Question Answering via Integer Programming over Semi-Structured Knowledge (2016, methodology): “…the capital of France?”) using document collections or databases (e.g., Freebase [Bollacker et al. , 2008], NELL [Carlson et al. , 2010]), for example [Brill et al. , 2002; Fader et al. , 2014; Ferrucci et al. , 2010; Ko et al. , 2007; Yih et al. , 2014; Yao and Van Durme, 2014; Zou et al. , 2014].”
Towards a Visual Turing Challenge (2014, background): “The p rogress in both fields has inspired researchers to build holistic architectures for challengi ng grounding [14, 15], natural language generation from image/video [16, 17, 18], image-to-sentence a lignment [19, 20, 21, 22], and recently presented question-answering problems [23, 24, 25, 26, 27]”
BERT Post-Training for Review Reading Comprehension and Aspect-based Sentiment Analysis (2019, background): “…of products and services, it is very hard, if not impossible, to pre-compile an up-to-date and reliable knowledge base to cover a wide assortment of questions that customers may ask, such as in factoid-based KB-QA (Xu et al., 2016; Fader et al., 2014; Kwok et al., 2001; Yin et al., 2015).”