Jiajun Bu


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Translate the Beauty in Songs: Jointly Learning to Align Melody and Translate Lyrics
Chengxi Li | Kai Fan | Jiajun Bu | Boxing Chen | Zhongqiang Huang | Zhi Yu
Findings of the Association for Computational Linguistics: EMNLP 2023

Song translation requires both translation of lyrics and alignment of music notes so that the resulting verse can be sung to the accompanying melody, which is a challenging problem that has attracted some interests in different aspects of the translation process. In this paper, we propose Lyrics-Melody Translation with Adaptive Grouping (LTAG), a holistic solution to automatic song translation by jointly modeling lyric translation and lyrics-melody alignment. It is a novel encoder-decoder framework that can simultaneously translate the source lyrics and determine the number of aligned notes at each decoding step through an adaptive note grouping module. To address data scarcity, we commissioned a small amount of training data annotated specifically for this task and used large amounts of automatic training data through back-translation. Experiments conducted on an English-Chinese song translation data set show the effectiveness of our model in both automatic and human evaluations.

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GEM: Gestalt Enhanced Markup Language Model for Web Understanding via Render Tree
Zirui Shao | Feiyu Gao | Zhongda Qi | Hangdi Xing | Jiajun Bu | Zhi Yu | Qi Zheng | Xiaozhong Liu
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

Inexhaustible web content carries abundant perceptible information beyond text. Unfortunately, most prior efforts in pre-trained Language Models (LMs) ignore such cyber-richness, while few of them only employ plain HTMLs, and crucial information in the rendered web, such as visual, layout, and style, are excluded. Intuitively, those perceptible web information can provide essential intelligence to facilitate content understanding tasks. This study presents an innovative Gestalt Enhanced Markup (GEM) Language Model inspired by Gestalt psychological theory for hosting heterogeneous visual information from the render tree into the language model without requiring additional visual input. Comprehensive experiments on multiple downstream tasks, i.e., web question answering and web information extraction, validate GEM superiority.

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Training Simultaneous Speech Translation with Robust and Random Wait-k-Tokens Strategy
Linlin Zhang | Kai Fan | Jiajun Bu | Zhongqiang Huang
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

Simultaneous Speech Translation (SimulST) is a task focused on ensuring high-quality translation of speech in low-latency situations. Despite this, the modality gap (e.g., unknown word boundaries) between audio and text presents a challenge. This gap hinders the effective application of policies from simultaneous text translation (SimulMT) and compromises the performance of offline speech translation. To address this issue, we first leverage the Montreal Forced Aligner (MFA) and utilize audio transcription pairs in pre-training the acoustic encoder, and introduce a token-level cross-modal alignment that allows the wait-k policy from SimulMT to better adapt to SimulST. This token-level boundary alignment simplifies the decision-making process for predicting read/write actions, as if the decoder were directly processing text tokens. Subsequently, to optimize the SimulST task, we propose a robust and random wait-k-tokens strategy. This strategy allows a single model to meet various latency requirements and minimizes error accumulation of boundary alignment during inference. Our experiments on the MuST-C dataset show that our method achieves better trade-off between translation quality and latency.


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Opinion Word Expansion and Target Extraction through Double Propagation
Guang Qiu | Bing Liu | Jiajun Bu | Chun Chen
Computational Linguistics, Volume 37, Issue 1 - March 2011


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Exploration of Term Dependence in Sentence Retrieval
Keke Cai | Jiajun Bu | Chun Chen | Kangmiao Liu
Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions

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Manifolds Based Emotion Recognition in Speech
Mingyu You | Chun Chen | Jiajun Bu | Jia Liu | Jianhua Tao
International Journal of Computational Linguistics & Chinese Language Processing, Volume 12, Number 1, March 2007: Special Issue on Affective Speech Processing