United States

In spoken question answering, the systems are designed to answer questions from contiguous text spans within the related speech transcripts. However, the most natural way that human seek or test their knowledge is via human conversations. Therefore, we propose a new Spoken Conversational Question Answering task (SCQA), aiming at enabling the systems to model complex dialogues flow given the speech documents. In this task, our main objective is to build the system to deal with conversational questions based on the audio recordings, and to explore the plausibility of providing more cues from different modalities with systems in information gathering. To this end, instead of directly adopting automatically generated speech transcripts with highly noisy data, we propose a novel unified data distillation approach, DDNet, which effectively ingests cross-modal information to achieve fine-grained representations of the speech and language modalities. Moreover, we propose a simple and novel mechanism, termed Dual Attention, by encouraging better alignments between audio and text to ease the process of knowledge transfer. To evaluate the capacity of SCQA systems in a dialogue-style interaction, we assemble a Spoken Conversational Question Answering (Spoken-CoQA) dataset with more than 40k question-answer pairs from 4k conversations. We first show that the performance of the existing state-of-the-art methods significantly degrade on our dataset, hence demonstrating the necessity of incorporating cross-modal information to achieve good performance gains. Our experimental results demonstrate that our proposed method achieves superior performance in spoken conversational question answering. Codes and datasets will be made publicly available.

NAACL 2022

End-to-end Spoken Conversational Question Answering: Task, Dataset and Model

spoken conversational question answering

multimodal representation learning

knowledge distillation

## Welcome to NAACL 2022!
The Annual Meeting of the North American Chapter of the Association for Computational Linguistics (NAACL) is taking place July 10-15, 2022 as a hybrid event, in Seattle, WA and online. We are happy to welcome all of you to conference! 
 
The main conference program features oral presentations, in-person and virtual posters and demo sessions, a plenary session for our best paper presentations and awards, keynote presentations, a plenary panel on the place of linguistics and symbolic structures in NLP, and an Industrial Track panel on Careers in NLP.
 
Posters (including Findings of NAACL 2022) and demos are grouped by areas for both the in-person and the virtual sessions. For the virtual component, the talks will be on Zoom and the posters and the demos will be in GatherTown. The Student Research Workshop and will have an oral session and a poster session.
 
The program also features 6 Tutorials and 26 Workshops.
 
 We wish you a wonderful conference!
 
[The NAACL 2022 Organizing Committee](https://2022.naacl.org/committees/organization/)

2022 Annual Conference of the North American Chapter of the Association for Computational Linguistics

poster

Scalable and Robust Self-Learning for Skill Routing in Large-Scale Conversational AI Systems

AB/BA analysis: A framework for estimating keyword spotting recall improvement while maintaining audio privacy

Temporal Generalization for Spoken Language Understanding

An End-to-End Dialogue Summarization System for Sales Calls

Controlled Data Generation via Insertion Operations for NLU

Easy and Efficient Transformer: Scalable Inference Solution For Large NLP Model

Efficient Semi-supervised Consistency Training for Natural Language Understanding

Distantly Supervised Aspect Clustering And Naming For E-Commerce Reviews

CULG: Commercial Universal Language Generation

Constraining word alignments with posterior regularization for label transfer

https://d3smihljt9218e.cloudfront.net/transcript/36161/subtitle/058170db7131acf445ad0a7d33861a5d.vtt

English (automatic)

End-to-end Spoken Conversational Question Answering: Task, Dataset and Model

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Scalable and Robust Self-Learning for Skill Routing in Large-Scale Conversational AI Systems

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