| Challenge: | Existing representation learning methods such as Word2vec represent word embeddings in the semantic space. |
| Approach: | They propose an efficient method for searching vectors via a non-metric matching function: inner product. |
| Outcome: | Experiments on data representations learned for different machine learning tasks show the proposed method outperforms existing methods. |
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Kee Kiat Koo, Ashutosh Joshi, Nishaanth Reddy, Karim Bouyarmane, Ismail Tutar, Vaclav Petricek, Changhe Yuan
| Challenge: | e-commerce search engines use customer behavior signals to augment lexical matching and improve search relevance. |
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Neural Network based Extreme Classification and Similarity Models for Product Matching (N18-3)
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| Challenge: | Matching a seller listed item to an appropriate product has become a fundamental step for e-commerce platforms. |
| Approach: | They propose to use a shallow neural network to match a seller's item to an appropriate product . they also propose a similarity approach based on deep siamese network to train and infer product information. |
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Graph-based Multilingual Product Retrieval in E-Commerce Search (2021.naacl-industry)
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| Challenge: | Modern e-commerce search systems require product retrieval under multilingual scenarios. |
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Efficient Nearest Neighbor Search for Cross-Encoder Models using Matrix Factorization (2022.emnlp-main)
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| Challenge: | Efficient k-nearest neighbor search is a fundamental task, foundational for many problems in NLP. |
| Approach: | They propose an approach that avoids the use of a dual-encoder for retrieval, relying solely on the cross-encoding model. |
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CoRT: Complementary Rankings from Transformers (2021.naacl-main)
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| Challenge: | Recent approaches to information retrieval mitigate computational costs by using a multi-stage ranking pipeline. |
| Approach: | They propose a ranking model that leverages contextual representations from pre-trained language models to complement term-based ranking functions while causing no significant delay at query time. |
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Reasoning Graph Enhanced Exemplars Retrieval for In-Context Learning (2025.coling-main)
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| Challenge: | Existing methods focus on semantic similarity between queries and candidate exemplars, while logical connections between reasoning steps can be beneficial to depict problem-solving process. |
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COIL: Revisit Exact Lexical Match in Information Retrieval with Contextualized Inverted List (2021.naacl-main)
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| Challenge: | Recent neural IR models shift towards soft matching all query document terms, but they lose the computation efficiency of exact match systems. |
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Proceedings of the Thirteenth Workshop on Graph-Based Methods for Natural Language Processing (TextGraphs-13) (D19-53)
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Centrality-aware Product Retrieval and Ranking (2024.emnlp-industry)
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| Challenge: | Ambiguity and complexity of user queries often lead to mismatch between user’s intent and retrieved product titles or documents. |
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Proceedings of the 2nd Workshop on Deep Learning Approaches for Low-Resource NLP (DeepLo 2019) (D19-61)
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| Challenge: | EMNLP-IJCNLP 2019 Workshop on Deep Learning Approaches for Low-Resource Natural Language Processing takes place in Hong Kong, China . |
| Approach: | EMNLP-IJCNLP 2019 Workshop on Deep Learning Approaches for Low-Resource Natural Language Processing takes place in Hong Kong, China . call for papers for this second workshop met with a strong response . |
| Outcome: | the EMNLP-IJCNLP 2019 workshop on deep learning approaches for low-resource natural language processing takes place in Hong Kong, China. |