Disfluency Correction using Unsupervised and Semi-supervised Learning (2021)

Abstract: Spoken language is different from the written language in its style and structure. Disfluencies that appear in transcriptions from speech recognition systems generally hamper the performance of downstream NLP tasks. Thus, a disfluency correction system that converts disfluent to fluent text is of great value. This paper introduces a disfluency correction model that translates disfluent to fluent text by drawing inspiration from recent encoder-decoder unsupervised style-transfer models for text. We also show considerable benefits in performance when utilizing a small sample of 500 parallel disfluent-fluent sentences in a semi-supervised way. Our unsupervised approach achieves a BLEU score of 79.39 on the Switchboard corpus test set, with further improvement to a BLEU score of 85.28 with semi-supervision. Both are comparable to two competitive fully-supervised models.

## introduction disfluencies are disruptions to the regular flow of speech, typically occurring in conversational speech. they include filler pauses such as uh and um, word repetitions, irregular elongations, discourse markers, conjunctions, and restarts. for example, the disfluent sentence "well we're actually uh we're getting ready" has its fluent form as, "we're getting ready". here, the words highlighted in green, blue and red refer to discourse, filler and restart disfluencies, respectively. disfluencies in the text can alter its syntactic and semantic structure, thereby adversely affecting the performance of downstream nlp tasks such as information extraction, summarization, translation, and parsing @xcite . these tasks also employ pretrained language models that are typically trained * joint first authors to expect fluent text. this motivates the need for disfluency correction systems that convert disfluent to fluent text. prior work has predominantly focused on the problem of disfluency detection @xcite @xcite . inspired by recent work on unsupervised machine translation and style-transfer models for text, we propose an unsupervised encoder-decoder based model to tackle the problem of disfluency correction. our model does not require access to a parallel corpus of disfluent and fluent sentences. we also show a semi-supervised variant of our model that uses a small amount of parallel disfluent-fluent text and significantly improves performance. to our knowledge, this is the first work to use stateof-the-art unsupervised models for the task of disfluency correction. our main contributions are as follows: ## our approach we draw inspiration from unsupervised neural machine translation models @xcite and style transfer models @xcite to design the disfluency correction model illustrated in fig-ure 1a. it consists of a single encoder and a single decoder, used to translate in both directions, i.e., from disfluent to fluent text and vice-versa. the decoder is additionally conditioned using a domain embedding to convey the direction of translation, signifying whether the input to the encoder is a fluent or disfluent sentence. more details about our framework are described below. ## experiments and results in this work, we use the switchboard corpus @xcite ) that includes telephonic conversations and their disfluency annotations @xcite . we create a 70:15:15 train, test, and validation split. the train set contains 110,964 sentences, whereas validation and test sets have 11,889 disfluent-fluent sentence pairs each. ## conclusion we propose an unsupervised disfluency correction model drawing motivation from prior work on unsupervised machine translation and style transfer. we investigate two kinds of domain embeddings for our model. we also present a semi-supervised disfluency correction approach. we finetune our model using only about 500 parallel sentences, which comes very close in performance (based on bleu scores) to a state-of-the-art, fully supervised system. in future work, we intend to explore how these techniques can be integrated more closely with spoken translation.