Discriminative Learning over Constrained Latent Representations

By Ming-Wei Chang, Dan Goldwasser, Dan Roth, Vivek Srikumar. 2010

83 citations recorded by the source corpus.

Subfields: Representation & Self-Supervised Learning

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Citations in the corpus

A Decomposable Attention Model for Natural Language Inference (2016, methodology): “Our method is motivated by the central role played by alignment in machine translation (Koehn, 2009) and previous approaches to sentence similarity modeling (Haghighi et al., 2005; Das and Smith, 2009; Chang et al., 2010; Fader et al., 2013), natural language inference (Marsi and Krahmer, 2005; MacC”
Semantic Parsing via Paraphrasing (2014, background): “Many existing paraphrase models introduce latent variables to describe the derivation of c from x , e.g., with transformations (Heilman and Smith, 2010; Stern and Dagan, 2011) or alignments (Haghighi et al., 2005; Das and Smith, 2009; Chang et al., 2010).”
ABCNN: Attention-Based Convolutional Neural Network for Modeling Sentence Pairs (2015, background): “c representation, Yih et al. (2013) turn their attention to improving the shallow semantic component, lexical semantics, by performing semantic matching based on a latent wordalignment structure (cf. Chang et al. (2010)). Lai and Hockenmaier (2014) explore more ﬁne-grained word overlap and alignment”
Question Answering via Integer Programming over Semi-Structured Knowledge (2016, methodology): “ILP based discrete optimization has been successful in several NLP tasks [ Roth and Yih, 2004; Chang et al. , 2010; Berant et al. , 2010; Srikumar and Roth, 2011; Goldwasser and Roth, 2011 ] .”
Question Answering as Global Reasoning Over Semantic Abstractions (2018, background): “Search-based reasoning systems have been successful in several NLP areas (Roth and Yih 2004; Chang et al. 2010; Berant, Dagan, and Goldberger 2010; Srikumar and Roth 2011; Goldwasser and Roth 2011; Schüller 2014; Fei et al. 2015).”
Neural Paraphrase Identification of Questions with Noisy Pretraining (2017, background): “Paraphrase identification is a well-studied task in NLP (Das and Smith, 2009; Chang et al., 2010; He et al., 2015; Wang et al., 2016, inter alia).”
Multi-Level Structured Models for Document-Level Sentiment Classification (2010, methodology): “Recently, researchers within both Natural Language Processing (e.g., Petrov and Klein (2007), Chang et al. (2010), Clarke et al. (2010)) and other ﬁelds (e.g., Felzenszwalb et al. (2008), Yu and Joachims (2009)) have analyzed joint multi-level models (i.e., models that simultaneously solve the main…”
Question Answering Using Enhanced Lexical Semantic Models (2013, methodology): “For the structured-output setting, we used the framework of learning constrained latent representation (LCLR) and required that each question word needed to be mapped to a word in the sentence.”
Driving Semantic Parsing from the World’s Response (2010, background): “The direct approach resembles learning a binary classifier over a latent structure (Chang et al., 2010a); while the aggressive approach has similarities with work that uses labeled structures and a binary signal indicating the existence of good structures to improve structured prediction (Chang et…”
Erratum: “ABCNN: Attention-Based Convolutional Neural Network for Modeling Sentence Pairs” (2016, background): “Instead of focusing on the high-level semantic representation, Yih et al. (2013) turn their attention to improving the shallow semantic component, lexical semantics, by performing semantic matching based on a latent word-alignment structure (cf. Chang et al. (2010)).”