Personalized neural language models for real-world query auto completion (N18-3)
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| Challenge: | Existing popularity-based methods for query auto completion (QAC) are ineffective in predicting unseen queries. |
| Approach: | They propose to use real-world data to build an end-to-end system that can predict unseen queries by integrating user information. |
| Outcome: | The proposed methods improve on two separate datasets while increasing diversity while scalability. |
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| Challenge: | Query auto-completion (QAC) is a search engine feature that suggests completed queries as the user types . recent work suggests personalization of the recurrent layer to generate personalized completions. |
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| Challenge: | Current neural query auto-completion systems rely on character-level language models but they slow down when queries are long. |
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| Challenge: | Existing systems that provide a graphical representation of QAC are limited in their ability to provide real-time data. |
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Voice Query Auto Completion (2021.emnlp-main)
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Raphael Tang, Karun Kumar, Kendra Chalkley, Ji Xin, Liming Zhang, Wenyan Li, Gefei Yang, Yajie Mao, Junho Shin, Geoffrey Craig Murray, Jimmy Lin
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| Challenge: | Language models such as GPT-2 require considerable training effort to adapt to specific writing domains (e.g., medical). |
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| Challenge: | Recent work has shown that statistical language modeling with transformers can greatly improve the performance in code completion tasks. |
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AUTOSUMM: Automatic Model Creation for Text Summarization (2021.emnlp-main)
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Sharmila Reddy Nangi, Atharv Tyagi, Jay Mundra, Sagnik Mukherjee, Raj Snehal, Niyati Chhaya, Aparna Garimella
| Challenge: | Recent efforts to develop deep learning models for text generation tasks are challenging for non-experts. |
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Rethinking Word-Level Auto-Completion in Computer-Aided Translation (2023.emnlp-main)
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| Challenge: | Existing models for word-level auto-completion (WLAC) do not meet the criterion of good auto-completes. |
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