Papers by Baijun Ji
Bilingual Dictionary Based Neural Machine Translation without Using Parallel Sentences (2020.acl-main)
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| Challenge: | a monolingual speaker can learn to translate by looking up a bilingual dictionary . a novel task of machine translation (MT) is based on no parallel sentences but can refer to a ground-truth bilingual dictionary and large-scale monolingual corpora. |
| Approach: | They propose a task of machine translation that uses a bilingual dictionary and large-scale monolingual corpora to translate a monolingual speaker. |
| Outcome: | The proposed task is based on a bilingual dictionary and large scale monolingual corpora, while being independent on parallel sentences. |
Combining Static Word Embeddings and Contextual Representations for Bilingual Lexicon Induction (2021.findings-acl)
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| Challenge: | Bilingual Lexicon Induction (BLI) aims to map words in one language to their translations in another. |
| Approach: | They propose a mechanism to combine static word embeddings and contextual representations to utilize the advantages of both paradigms. |
| Outcome: | The proposed method improves performance on supervised and unsupervised BLI benchmarks on all language pairs by average improving 3.2 points over baselines. |
Increasing Visual Awareness in Multimodal Neural Machine Translation from an Information Theoretic Perspective (2022.emnlp-main)
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| Challenge: | Existing studies focus on extracting multi-granularity visual features for integration or designing model architectures for better message passing across various modalities. |
| Approach: | They propose to decompose the informative visual signals into two parts: source-specific information and target-specific info. |
| Outcome: | The proposed method can enhance the visual awareness of MMT models against strong baselines. |
Submodular-based In-context Example Selection for LLMs-based Machine Translation (2024.lrec-main)
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| Challenge: | Prior studies have focused on the role of well-chosen examples in in-context learning . |
| Approach: | They propose to use multiple translational factors for in-context example selection by using monotone submodular function maximization. |
| Outcome: | The proposed approach outperforms random selection and robust single-factor baselines across various NLP tasks. |
CPRM: A LLM-based Continual Pre-training Framework for Relevance Modeling in Commercial Search (2025.naacl-industry)
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Kaixin Wu, Yixin Ji, Zeyuan Chen, Qiang Wang, Cunxiang Wang, Hong Liu, Baijun Ji, Xu Jia, Zhongyi Liu, Jinjie Gu, Yuan Zhou, Linjian Mo
| Challenge: | Relevance modeling between queries and items is a key component of commercial search engines. |
| Approach: | They propose a framework for continual pre-training of LLMs to enhance domain knowledge . they employ queries and multi-field item to jointly pre-train for enhancing domain knowledge. |
| Outcome: | The proposed model achieves convincing performance compared to strong baselines. |