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We propose models for semantic orienta- tions of phrases as well as classification methods based on the models. Although each phrase consists of multiple words, the semantic orientation of the …...

We formulate dependency parsing as a graphical model with the novel ingredient of global constraints. We show how to apply loopy belief propagation (BP), a simple and effective tool for …...

String-to-string transduction is a central prob- lem in computational linguistics and natural language processing. It occurs in tasks as di- verse as name transliteration, spelling correc- tion, pronunciation modeling and …...

Conversations provide rich opportunities for interactive, continuous learning. When some- thing goes wrong, a system can ask for clari- fication, rewording, or otherwise redirect the interaction to achieve its goals. …...

We consider a semi-supervised setting for do- main adaptation where only unlabeled data is available for the target domain. One way to tackle this problem is to train a generative …...

Distant supervision for relation extraction (RE) – gathering training data by aligning a database of facts with text – is an efficient ap- proach to scale RE to thousands of …...

Domain adaptation has been popularly stud- ied on exploiting labeled information from a source domain to learn a prediction model in a target domain. In this paper, we develop a …...

Many models in NLP involve latent vari- ables, such as unknown parses, tags, or alignments. Finding the optimal model pa- rameters is then usually a difficult noncon- vex optimization problem. …...

In this article, we describe a new approach to distributional semantics. This approach relies on a generative model of sentences with latent variables, which takes the syntax into account by …...

This paper discusses the use of factoriza- tion techniques in distributional semantic models. We focus on a method for re- distributing the weight of latent variables, which has previously been …...

AbstractWe explore how to detect people’s perspectives that occupy a certain proposition. We propose a Bayesian modelling approach where topics (or propositions) and their associated perspectives (or viewpoints) are modeled …...

AbstractWhile recent neural encoder-decoder models have shown great promise in modeling open-domain conversations, they often generate dull and generic responses. Unlike past work that has focused on diversifying the output …...

AbstractNeural models have been investigated for sentiment classification over constituent trees. They learn phrase composition automatically by encoding tree structures but do not explicitly model sentiment composition, which requires to …...

AbstractWhen constructing models that learn from noisy labels produced by multiple annotators, it is important to accurately estimate the reliability of annotators. Annotators may provide labels of inconsistent quality due …...

AbstractWe conduct a large scale empirical investigation of contextualized number prediction in running text. Specifically, we consider two tasks: (1)masked number prediction– predict-ing a missing numerical value within a sentence, …...

AbstractThe neural attention model has achieved great success in data-to-text generation tasks. Though usually excelling at producing fluent text, it suffers from the problem of information missing, repetition and “hallucination”. …...

AbstractConditional Variational AutoEncoder (CVAE) effectively increases the diversity and informativeness of responses in open-ended dialogue generation tasks through enriching the context vector with sampled latent variables. However, due to the …...

AbstractThe past several years have witnessed Variational Auto-Encoder’s superiority in various text generation tasks. However, due to the sequential nature of the text, auto-regressive decoders tend to ignore latent variables …...

AbstractGrounding dialogue generation by extra knowledge has shown great potentials towards building a system capable of replying with knowledgeable and engaging responses. Existing studies focus on how to synthesize a …...

AbstractWe propose an explainable approach for relation extraction that mitigates the tension between generalization and explainability by jointly training for the two goals. Our approach uses a multi-task learning architecture, …...

AbstractMulti-party dialogues are more difficult for models to understand than one-to-one two-party dialogues, since they involve multiple interlocutors, resulting in interweaving reply-to relations and information flows. To step over these …...