Current disfluency detection models focus on individual utterances each from a single speaker.However, numerous discontinuity phenomena in spoken conversational transcripts occur across multiple turns, which can not be identified by disfluency detection models.This study addresses these phenomena by proposing an innovative Multi-Turn Cleanup task for spoken conversational transcripts and collecting a new dataset, MultiTurnCleanup 1 .We design a data labeling schema to collect the high-quality dataset and provide extensive data analysis.Furthermore, we leverage two modeling approaches for experimental evaluation as benchmarks for future research.

Angelica Chen, Vicky Zayats, Daniel Walker, Dirk Padfield. Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 2022.

Tables in Web documents are pervasive and can be directly used to answer many of the queries searched on the Web, motivating their integration in question answering. Very often information presented in tables is succinct and hard to interpret with standard language representations. On the other hand, tables often appear within textual context, such as an article describing the table. Using the information from an article as additional context can potentially enrich table representations. In this work we aim to improve question answering from tables by refining table representations based on information from surrounding text. We also present an effective method to combine text and table-based predictions for question answering from full documents, obtaining significant improvements on the Natural Questions dataset.

Automatic Speech Recognition (ASR) systems are often optimized to work best for speakers with canonical speech patterns.Unfortunately, these systems perform poorly when tested on atypical speech and heavily accented speech.It has previously been shown that personalization through model fine-tuning substantially improves performance.However, maintaining such large models per speaker is costly and difficult to scale.We show that by adding a relatively small number of extra parameters to the encoder layers via socalled residual adapter, we can achieve similar adaptation gains compared to model finetuning, while only updating a tiny fraction (less than 0.5%) of the model parameters.We demonstrate this on two speech adaptation tasks (atypical and accented speech) and for two state-of-the-art ASR architectures.

Disfluency detection models now approach high accuracy on English text. However, little exploration has been done in improving the size and inference time of the model. At the same time, automatic speech recognition (ASR) models are moving from server-side inference to local, on-device inference. Supporting models in the transcription pipeline (like disfluency detection) must follow suit. In this work we concentrate on the disfluency detection task, focusing on small, fast, on-device models based on the BERT architecture. We demonstrate it is possible to train disfluency detection models as small as 1.3 MiB, while retaining high performance. We build on previous work that showed the benefit of data augmentation approaches such as self-training. Then, we evaluate the effect of domain mismatch between conversational and written text on model performance. We find that domain adaptation and data augmentation strategies have a more pronounced effect on these smaller models, as compared to conventional BERT models.

This paper explores contexts associated with errors in transcrip-tion of spontaneous speech, shedding light on human perceptionof disfluencies and other conversational speech phenomena. Anew version of the Switchboard corpus is provided with disfluency annotations for careful speech transcripts, together with results showing the impact of transcription errors on evaluation of automatic disfluency detection.

This paper presents a novel approach for modeling threaded discussions on social media using a graph-structured bidirectional LSTM (long-short term memory) which represents both hierarchical and temporal conversation structure. In experiments with a task of predicting popularity of comments in Reddit discussions, the proposed model outperforms a node-independent architecture for different sets of input features. Analyses show a benefit to the model over the full course of the discussion, improving detection in both early and late stages. Further, the use of language cues with the bidirectional tree state updates helps with identifying controversial comments.

We introduce a new approach for disfluency detection using a Bidirectional Long-Short Term Memory neural network (BLSTM). In addition to the word sequence, the model takes as input pattern match features that were developed to reduce sensitivity to vocabulary size in training, which lead to improved performance over the word sequence alone. The BLSTM takes advantage of explicit repair states in addition to the standard reparandum states. The final output leverages integer linear programming to incorporate constraints of disfluency structure. In experiments on the Switchboard corpus, the model achieves state-of-the-art performance for both the standard disfluency detection task and the correction detection task. Analysis shows that the model has better detection of non-repetition disfluencies, which tend to be much harder to detect.

This paper addresses the question of how language use affects community reaction to comments in online discussion forums, and the relative importance of the message vs. the messenger.A new comment ranking task is proposed based on community annotated karma in Reddit discussions, which controls for topic and timing of comments.Experimental work with discussion threads from six subreddits shows that the importance of different types of language features varies with the community of interest.

Victoria Zayats, Mari Ostendorf, Hannaneh Hajishirzi. Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 2015.

The ATAROS project aims to identify acoustic signals of stance-taking in order to inform the development of automatic stance recognition in natural speech. Due to the typically low frequency of stance-taking in existing corpora that have been used to investigate related phenomena such as subjectivity, we are creating an audio corpus of unscripted conversations between dyads as they complete collaborative tasks designed to elicit a high density of stance-taking at increasing levels of involvement. To validate our experimental design and provide a preliminary assessment of the corpus, we examine a fully transcribed and time-aligned portion to compare the speaking styles in two tasks, one expected to elicit low involvement and weak stances, the other high involvement and strong stances. We find that although overall measures such as task duration and total word count do not indicate consistent differences across tasks, speakers do display significant differences in speaking style. Factors such as increases in speaking rate, turn length, and disfluencies from weakto strong-stance tasks are consistent with increased involvement by the participants and provide evidence in support of the experimental design.

This paper investigates automatic detection of different types of self-repairs in spontaneous speech under different social contexts, from casual conversations to government hearings. The work shows that a simple CRF-based model is effective for cross-domain training, which is important for contexts where annotated data is not available. The approach explicitly represents common types of disfluencies observed in multi-domain data both in the model state space and the features extracted. In addition, the model incorporates an expanded state space for recognizing the repair structure, unlike prior work that annotates only the reparandum.