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ParaNMT-50M: Pushing the Limits of Paraphrastic Sentence Embeddings with Millions of Machine Translations
John Wieting, Kevin Gimpel
We describe PARANMT-50M, a dataset of more than 50 million English-English sentential paraphrase pairs. We generated the pairs automatically by using neural machine translation to…
A Study of All-Convolutional Encoders for Connectionist Temporal Classification
Kalpesh Krishna, Liang Lu, Kevin Gimpel +1
Connectionist temporal classification (CTC) is a popular sequence prediction approach for automatic speech recognition that is typically used with models based on recurrent neural…
End-to-End Neural Segmental Models for Speech Recognition
Hao Tang, Liang Lu, Lingpeng Kong +5
Segmental models are an alternative to frame-based models for sequence prediction, where hypothesized path weights are based on entire segment scores rather than a single frame at…
Learning to Embed Words in Context for Syntactic Tasks
Lifu Tu, Kevin Gimpel, Karen Livescu
We present models for embedding words in the context of surrounding words. Such models, which we refer to as token embeddings, represent the characteristics of a word that are spec…
Learning Paraphrastic Sentence Embeddings from Back-Translated Bitext
John Wieting, Jonathan Mallinson, Kevin Gimpel
We consider the problem of learning general-purpose, paraphrastic sentence embeddings in the setting of Wieting et al. (2016b). We use neural machine translation to generate senten…
Revisiting Recurrent Networks for Paraphrastic Sentence Embeddings
John Wieting, Kevin Gimpel
We consider the problem of learning general-purpose, paraphrastic sentence embeddings, revisiting the setting of Wieting et al. (2016b). While they found LSTM recurrent networks to…
Parsing Speech: A Neural Approach to Integrating Lexical and Acoustic-Prosodic Information
Trang Tran, Shubham Toshniwal, Mohit Bansal +3
In conversational speech, the acoustic signal provides cues that help listeners disambiguate difficult parses. For automatically parsing spoken utterances, we introduce a model tha…