Papers with context encoder
Multi-Hop Open-Domain Question Answering over Structured and Unstructured Knowledge (2022.findings-naacl)
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| Challenge: | Existing open-domain question answering systems only select one source to generate answer or conduct reasoning on structured information. |
| Approach: | They propose a Document-Entity Heterogeneous Graph Network to integrate different sources of information and conduct reasoning on heterogeneous information. |
| Outcome: | The proposed model outperforms the state-of-the-art methods on a HybirdQA dataset. |
Document-Level Neural Machine Translation Using BERT as Context Encoder (2020.aacl-srw)
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| Challenge: | Large-scale pre-trained representations such as BERT have been widely used in many natural language understanding tasks. |
| Approach: | They propose to use BERT as a context encoder to achieve document-level contextual information, which is then integrated into both the encoder and decoder. |
| Outcome: | The proposed model outperforms strong document-level machine translation baselines on BLEU score and captures document- level context information to boost translation performance. |
Improving the Transformer Translation Model with Document-Level Context (D18-1)
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| Challenge: | Existing models for document-level context translation ignore documentlevel context. |
| Approach: | They propose a document-level context encoder to represent document- level context and integrate it into the Transformer model. |
| Outcome: | Experiments on NIST Chinese-English and IWSLT French-English datasets show that the proposed translation model outperforms the Transformer model significantly. |
Learn To Remember: Transformer with Recurrent Memory for Document-Level Machine Translation (2022.findings-naacl)
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| Challenge: | Recent studies have shown that the effective use of contextual information between sentences can achieve better performance in document-level machine translation. |
| Approach: | They propose a recurrent memory unit to the Transformer to support the information exchange between the sentence and previous context. |
| Outcome: | The proposed model outperforms the previous work on TED and News by 0.91 s-BLEU and 1.49 d-BLUE on average. |
Does Multi-Encoder Help? A Case Study on Context-Aware Neural Machine Translation (2020.acl-main)
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| Challenge: | In encoder-decoder neural models, multiple encoders are used to represent contextual information in addition to the individual sentence. |
| Approach: | They propose to use multiple context encoders to encode the individual sentences in document-level neural machine translation (NMT) They propose a noisy dropout setup and a single-encoder approach to encode context sentences. |
| Outcome: | The proposed approach encodes the context and the current sentence without contexts. |
MTCue: Learning Zero-Shot Control of Extra-Textual Attributes by Leveraging Unstructured Context in Neural Machine Translation (2023.findings-acl)
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| Challenge: | Existing research has focused on providing individual, well-defined types of context in translation, such as the surrounding text or discrete external variables like the speaker’s gender. |
| Approach: | They introduce a novel neural machine translation framework that interprets all context as text. |
| Outcome: | The proposed framework outperforms a baseline that matched the parameters and significantly outperformed it in English translation. |
Unsupervised Neural Word Segmentation for Chinese via Segmental Language Modeling (D18-1)
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| Challenge: | Existing approaches to unsupervised Chinese word segmentation (CWS) are discriminative and generative, but they are non-trivial. |
| Approach: | They propose a neural generative model for fully unsupervised Chinese word segmentation (CWS) their approach explicitly focuses on the segmental nature of Chinese, and preserves several properties of language models. |
| Outcome: | The proposed model achieves competitive performance to the state-of-the-art models on four datasets from SIGHAN 2005 bakeoff. |