Learning a Word-Level Language Model with Sentence-Level Noise Contrastive Estimation for Contextual Sentence Probability Estimation

• URL: http://arxiv.org/abs/2103.07875v1
• Date: Sun, 14 Mar 2021 09:17:37 GMT
• Title: Learning a Word-Level Language Model with Sentence-Level Noise Contrastive Estimation for Contextual Sentence Probability Estimation
• Authors: Heewoong Park, Sukhyun Cho, Jonghun Park
• Abstract summary: Inferring the probability distribution of sentences or word sequences is a key process in natural language processing. While word-level language models (LMs) have been widely adopted for computing the joint probabilities of word sequences, they have difficulty capturing a context long enough for sentence probability estimation (SPE) Recent studies introduced training methods using sentence-level noise-contrastive estimation (NCE) with recurrent neural networks (RNNs) We apply our method to a simple word-level RNN LM to focus on the effect of the sentence-level NCE training rather than on the network architecture.
• Score: 3.1040192682787415
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Inferring the probability distribution of sentences or word sequences is a key process in natural language processing. While word-level language models (LMs) have been widely adopted for computing the joint probabilities of word sequences, they have difficulty in capturing a context long enough for sentence probability estimation (SPE). To overcome this, recent studies introduced training methods using sentence-level noise-contrastive estimation (NCE) with recurrent neural networks (RNNs). In this work, we attempt to extend it for contextual SPE, which aims to estimate a conditional sentence probability given a previous text. The proposed NCE samples negative sentences independently of a previous text so that the trained model gives higher probabilities to the sentences that are more consistent with \textcolor{blue}{the} context. We apply our method to a simple word-level RNN LM to focus on the effect of the sentence-level NCE training rather than on the network architecture. The quality of estimation was evaluated against multiple-choice cloze-style questions including both human and automatically generated questions. The experimental results show that the proposed method improved the SPE quality for the word-level RNN LM.

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