使用預訓練編碼器提升跨語言摘要能力

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姓名

莊家閔(Chia-Min Chuang) 查詢紙本館藏

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軟體工程研究所

論文名稱

使用預訓練編碼器提升跨語言摘要能力
(Improving Cross-Lingual Text Summarization using Pretrained Encoder)

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至系統瀏覽論文 (2025-7-1以後開放)

摘要(中)

跨語言文本摘要是透過機器將一種語言的文章轉換成另
一種語言的摘要，先前的研究大多將該任務以兩步驟方法處
理──「先翻譯後摘要」或「先摘要後翻譯」。但是，這兩
種方法皆會有翻譯錯誤的問題，且其中的機器翻譯模型難以
隨著摘要任務繼續更新微調（fine-tune）。針對上述問題，
我們採用預訓練跨語言編碼器以向量表示（represent）不同
語言的輸入，將其映射至相同的向量空間。預訓練方法已被
廣泛應用在各種自然語言生成任務中，並取得優異的模型表
現。此編碼器使得模型在學習摘要能力的過程中，同時保有
跨語言能力。本研究中，我們實驗三種不同的微調方法，
證明預訓練跨語言編碼器可以學習單詞階層（word-level）
的語意特徵。在我們所有的模型組態裡，最優異的模型可
在ROUGE-1分數上，超越基準模型3分。

摘要(英)

Cross-lingual text summarization (CLTS) is the task to generate a summary in one language given a document in a another language. Most of the previous work consider CLTS as two sub-tasks: translate-then-summarize and summarize-then-translate. Both of them are suffered from translation error and the translation system is hard to be fine-tuned with text summarization directly. To
deal with the above problems, we utilize a pretrained cross-lingual encoder, which has been demonstrated the effectiveness in natural language generation, to represent text inputs from from different languages. We augment a standard sequence-to-sequence (Seq2Seq) network with our pretrained cross-lingual encoder so as to capture cross-lingual contextualized word representation. We show that the pretrained cross-lingual encoder can be fine-tuned on a text summarization dataset while keeping the cross-lingual ability. We experiment three different fine-tune strategies and show that the pretrained encoder can capture cross-lingual semantic features. The best of the proposed models obtains 42.08 Rouge-1 on ZH2ENSUM datasets [Zhu et al., 2019], significantly improving
our baseline model by more than 3 Rouge-1.

關鍵字(中)

★ 文本摘要
★ 預訓練模型
★ 跨語言處理

關鍵字(英)

★ Summarization
★ Pretraining language model
★ Cross-lingual

論文目次

Chinese Abstract . . . . . . . . . . . . . . . . . . . . . . i
English Abstract . . . . . . . . . . . . . . . . . . . . . . ii
Acknowledgements . . . . . . . . . . . . . . . . . . . . iv
Content . . . . . . . . . . . . . . . . . . . . . . . . . . v
List of Figure . . . . . . . . . . . . . . . . . . . . . . . viii
List of Table . . . . . . . . . . . . . . . . . . . . . . . . x
1 Introduction . . . . . . . . . . . . . . . . . . 1
2 Related Work . . . . . . . . . . . . . . . . . 4
2.1 End-to-end Cross-lingual Language Generation
. . . . . . . . . . . . . . . . . . . 4
2.2 Cross-lingual Pretraining . . . . . . . . . . 6
3 Method . . . . . . . . . . . . . . . . . . . . 8
3.1 Transformer . . . . . . . . . . . . . . . . 8
3.2 Baseline Model . . . . . . . . . . . . . . . 10
3.3 Weights Transformation . . . . . . . . . . 12
v
3.4 Pretrained Cross-lingual Masked Language
Model . . . . . . . . . . . . . . . . . . . 12
3.5 Cross-lingual Contextualized Word Representations
. . . . . . . . . . . . . . . . . . 13
3.5.1 Cross-lingual Encoder (CLTS-XENC) . . . . . 15
3.5.2 Cross-lingual ELMo (CLTS-ELMo) . . . . . . 15
4 Experiments . . . . . . . . . . . . . . . . . . 17
4.1 Datasets . . . . . . . . . . . . . . . . . . 17
4.2 Evaluation Metrics . . . . . . . . . . . . . 18
4.3 Training Details . . . . . . . . . . . . . . 20
4.4 Result and Analysis . . . . . . . . . . . . . 21
4.4.1 Fine-Tuning Strategies . . . . . . . . . . . . 22
4.4.2 Pretraining Steps . . . . . . . . . . . . . . . 23
4.4.3 Cross-lingual Word Embeddings . . . . . . . 24
4.4.4 Human Evaluation . . . . . . . . . . . . . . 27
5 Conclusion and Future Work . . . . . . . . . 30
5.1 Conclusion . . . . . . . . . . . . . . . . . 30
5.2 Future Work . . . . . . . . . . . . . . . . 31
5.2.1 Adversarial Training . . . . . . . . . . . . . 32
5.2.2 Multi-task Learning . . . . . . . . . . . . . . 33
Appendix A . . . . . . . . . . . . . . . . . . . . . . . . 40
A.1 Cross-lingual Text Summarization Examples 40
Appendix B . . . . . . . . . . . . . . . . . . . . . . . . 43
vi
B.1 Round-Trip Translation . . . . . . . . . . . 43

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指導教授

蔡宗翰(Tzong-Han Tsai)

審核日期

2020-7-31

推文