Papers by Boliang Zhang
Platforms for Non-speakers Annotating Names in Any Language (P18-4)
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| Challenge: | Traditionally, native speakers of a language have been asked to annotate a corpus in that language. |
| Approach: | They propose two annotation platforms that allow an English speaker to annotate names for any language without knowing the language. |
| Outcome: | The proposed annotations achieved state-of-the-art performance on two surprise languages and ten languages at TAC-KBP EDL2017. |
ELISA-EDL: A Cross-lingual Entity Extraction, Linking and Localization System (N18-5)
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| Challenge: | ELISA-EDL is a cross-lingual entity extraction, linking and localization system for Wikipedia languages. |
| Approach: | They propose a cross-lingual entity extraction, linking and localization system for English speakers . it extracts entities from unstructured text in any of 282 Wikipedia languages and links them to English knowledge bases . |
| Outcome: | The proposed system extracts entity mentions from Wikipedia and links them to English knowledge bases and visualizes locations related to disaster topics on a world heatmap. |
Paper Abstract Writing through Editing Mechanism (P18-2)
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| Challenge: | a paper abstract writing system can automatically generate an abstract from a title . a typical recurrent neural network (RNN) based approach easily loses focus. |
| Approach: | They propose a paper abstract writing system that automatically generates an abstract from a title. |
| Outcome: | The proposed system passes Turing tests by junior domain experts and non-experts at a rate up to 80%. |
Error Analysis of Uyghur Name Tagging: Language-specific Techniques and Remaining Challenges (L18-1)
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| Challenge: | despite efforts at name tagging, there is limited understanding on the performance ceiling . despite the high-resource language, there are very few natural language processing tools available . |
| Approach: | They propose to use a machine learning model to identify Uyghur name tagger errors . they conclude that such a model is unlikely to be effective for Uygur, or low-resource languages . |
| Outcome: | The proposed model is unlikely to be effective for Uyghur, or low-resource languages in general, the authors argue . they show that the proposed model can be used for high-res languages with superficial features . |
Parallel Corpus Filtering via Pre-trained Language Models (2020.acl-main)
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| Challenge: | Existing methods to filter out noisy parallel sentences from web crawled data are in demand. |
| Approach: | They propose a method to filter out noisy sentence pairs from web crawled corpora using pre-trained language models. |
| Outcome: | The proposed method outperforms baselines and achieves state-of-the-art on two datasets. |
Genre Separation Network with Adversarial Training for Cross-genre Relation Extraction (D18-1)
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| Challenge: | Existing methods to extract genre-specific and genre-agnostic features require great human effort. |
| Approach: | They propose to use two encoders to explicitly extract genre-specific and genre-agnostic features. |
| Outcome: | The proposed approach outperforms the state-of-the-art by 1.7% on three distinct genres. |
Multi-lingual Common Semantic Space Construction via Cluster-consistent Word Embedding (D18-1)
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| Challenge: | a new approach to multilingual word embedding is needed to achieve this goal . a multilingual common semantic space is a language-agnostic semantic continuous space . |
| Approach: | They propose a multilingual common semantic space where words from multiple languages are mapped into a shared space so that resources and knowledge can be shared across languages. |
| Outcome: | The proposed approach achieves 14.6% absolute F-score gain over state-of-the-art methods on cross-lingual direct transfer. |
MeetDot: Videoconferencing with Live Translation Captions (2021.emnlp-demo)
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Arkady Arkhangorodsky, Christopher Chu, Scot Fang, Yiqi Huang, Denglin Jiang, Ajay Nagesh, Boliang Zhang, Kevin Knight
| Challenge: | MeetDot is a videoconferencing system with live translation captions overlaid on screen . currently, the system supports speech and captions in 4 languages . |
| Approach: | They propose a videoconferencing system with live translation captions overlaid on screen . the system supports speech and captions in 4 languages and combines automatic speech recognition and machine translation in a cascade . |
| Outcome: | The proposed system supports speech and captions in 4 languages and has very tight latency requirements to have acceptable call quality. |