SEAGLE: A Platform for Comparative Evaluation of Semantic Encoders for Information Retrieval (D19-3)
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| Challenge: | Existing semantic text encoding models are limited in coverage and few attempts to empirically compare them on IR tasks have been made. |
| Approach: | They propose to implement word embedding aggregators and pretrained semantic encoders and to allow for their comparative evaluation on arbitrary IR collections. |
| Outcome: | The proposed model can be exploited via an easy-to-use web interface and its modular backend (micro-service architecture) can easily be extended with additional semantic search models. |
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Ehsan Kamalloo, Xinyu Zhang, Odunayo Ogundepo, Nandan Thakur, David Alfonso-hermelo, Mehdi Rezagholizadeh, Jimmy Lin
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| Challenge: | Word and sentence similarity tasks are the de facto evaluation method for embeddings. |
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Wuttikorn Ponwitayarat, Peerat Limkonchotiwat, Raymond Ng, Jann Railey Montalan, Thura Aung, Jian Gang Ngui, Yosephine Susanto, William Chandra Tjhi, Panuthep Tasawong, Erik Cambria, Ekapol Chuangsuwanich, Sarana Nutanong
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| Challenge: | Recent advances in large language models have enabled their use as semantic encoders for recommendation, but their roles and behaviors in this setting are still not well understood. |
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| Challenge: | Existing word embeddings combine complementary strengths of their components to achieve unsupervised semantic similarity (STS). |
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Word Embedding Evaluation in Downstream Tasks and Semantic Analogies (2020.lrec-1)
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| Challenge: | Language Models (LMs) are an oft studied area of natural language processing . Word Embeddings (WE) are vector space representations of a vocabulary . |
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Multilingual Universal Sentence Encoder for Semantic Retrieval (2020.acl-demos)
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Yinfei Yang, Daniel Cer, Amin Ahmad, Mandy Guo, Jax Law, Noah Constant, Gustavo Hernandez Abrego, Steve Yuan, Chris Tar, Yun-hsuan Sung, Brian Strope, Ray Kurzweil
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SentEval: An Evaluation Toolkit for Universal Sentence Representations (L18-1)
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| Challenge: | a toolkit for evaluating the quality of universal sentence representations is available for download and preprocessing . word embeddings are not trained to perform well on one specific task, but their value lies in their transferability . evaluation of general-purpose word and sentence embeddables has been problematic . |
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Shall We Pretrain Autoregressive Language Models with Retrieval? A Comprehensive Study (2023.emnlp-main)
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Boxin Wang, Wei Ping, Peng Xu, Lawrence McAfee, Zihan Liu, Mohammad Shoeybi, Yi Dong, Oleksii Kuchaiev, Bo Li, Chaowei Xiao, Anima Anandkumar, Bryan Catanzaro
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