Jingxiang Chen - ACL Anthology ACL Anthology FAQ(current) Corrections(current) Submissions(current) Jingxiang Chen 2021 pdf bib abs Detect Profane Language in Streaming Services to Protect Young Audiences Jingxiang Chen | Kai Wei | Xiang Hao Proceedings of The 4th Workshop on e-Commerce and NLP With the rapid growth of online video streaming, recent years have seen increasing concerns about profane language in their content. Detecting profane language in streaming services is challenging due to the long sentences appeared in a video. While recent research on handling long sentences has focused on developing deep learning modeling techniques, little work has focused on techniques on improving data pipelines. In this work, we develop a data collection pipeline to address long sequence of texts and integrate this pipeline with a multi-head self-attention model. With this pipeline, our experiments show the self-attention model offers 12.5% relative accuracy improvement over state-of-the-art distilBERT model on profane language detection while requiring only 3% of parameters. This research designs a better system for informing users of profane language in video streaming services. 2020 pdf bib abs Automating Template Creation for Ranking-Based Dialogue Models Jingxiang Chen | Heba Elfardy | Simi Wang | Andrea Kahn | Jared Kramer Proceedings of the 2nd Workshop on Natural Language Processing for Conversational AI Dialogue response generation models that use template ranking rather than direct sequence generation allow model developers to limit generated responses to pre-approved messages. However, manually creating templates is time-consuming and requires domain expertise. To alleviate this problem, we explore automating the process of creating dialogue templates by using unsupervised methods to cluster historical utterances and selecting representative utterances from each cluster. Specifically, we propose an end-to-end model called Deep Sentence Encoder Clustering (DSEC) that uses an auto-encoder structure to jointly learn the utterance representation and construct template clusters. We compare this method to a random baseline that randomly assigns templates to clusters as well as a strong baseline that performs the sentence encoding and the utterance clustering sequentially. To evaluate the performance of the proposed method, we perform an automatic evaluation with two annotated customer service datasets to assess clustering effectiveness, and a human-in-the-loop experiment using a live customer service application to measure the acceptance rate of the generated templates. DSEC performs best in the automatic evaluation, beats both the sequential and random baselines on most metrics in the human-in-the-loop experiment, and shows promising results when compared to gold/manually created templates. Search Co-authors Kai Wei 1 Xiang Hao 1 Heba Elfardy 1 Simi Wang 1 Andrea Kahn 1 show all... Jared Kramer 1 Venues ECNLP1 NLP4ConvAI1 ACL materials are Copyright © 1963–2022 ACL; other materials are copyrighted by their respective copyright holders. Materials prior to 2016 here are licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 3.0 International License. Permission is granted to make copies for the purposes of teaching and research. Materials published in or after 2016 are licensed on a Creative Commons Attribution 4.0 International License. The ACL Anthology is managed and built by the ACL Anthology team of volunteers. Site last built on 22 September 2022 at 01:12 UTC with commit 3f1dbb15.