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20172022
most citedRecurrent Neural Network-Based Sentence Encoder with Gated Attention for Natural Language Inference

22 citations · 44 across the 7 of their papers we have counts for

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Showing cs.CLShow all

10 papers · 1 filter

cs.CL2021

Pre-training for Spoken Language Understanding with Joint Textual and Phonetic Representation Learning

Qian Chen, Wen Wang, Qinglin Zhang

In the traditional cascading architecture for spoken language understanding (SLU), it has been observed that automatic speech recognition errors could be detrimental to the perform…

cs.CL2021

Discriminative Self-training for Punctuation Prediction

Qian Chen, Wen Wang, Mengzhe Chen +1

Punctuation prediction for automatic speech recognition (ASR) output transcripts plays a crucial role for improving the readability of the ASR transcripts and for improving the per…

cs.CL20207 cited

Sequential Neural Networks for Noetic End-to-End Response Selection

Qian Chen, Wen Wang

The noetic end-to-end response selection challenge as one track in the 7th Dialog System Technology Challenges (DSTC7) aims to push the state of the art of utterance classification…

cs.CL20201 cited

Controllable Time-Delay Transformer for Real-Time Punctuation Prediction and Disfluency Detection

Qian Chen, Mengzhe Chen, Bo Li +1

With the increased applications of automatic speech recognition (ASR) in recent years, it is essential to automatically insert punctuation marks and remove disfluencies in transcri…

cs.CL20202 cited

Transfer Learning for Context-Aware Spoken Language Understanding

Qian Chen, Zhu Zhuo, Wen Wang +1

Spoken language understanding (SLU) is a key component of task-oriented dialogue systems. SLU parses natural language user utterances into semantic frames. Previous work has shown…

cs.CL2019

Align, Mask and Select: A Simple Method for Incorporating Commonsense Knowledge into Language Representation Models

Zhi-Xiu Ye, Qian Chen, Wen Wang +1

The state-of-the-art pre-trained language representation models, such as Bidirectional Encoder Representations from Transformers (BERT), rarely incorporate commonsense knowledge or…