Context-Aware Neural Machine Translation Learns Anaphora Resolution (1805.10163v1)

Abstract: Standard machine translation systems process sentences in isolation and hence ignore extra-sentential information, even though extended context can both prevent mistakes in ambiguous cases and improve translation coherence. We introduce a context-aware neural machine translation model designed in such way that the flow of information from the extended context to the translation model can be controlled and analyzed. We experiment with an English-Russian subtitles dataset, and observe that much of what is captured by our model deals with improving pronoun translation. We measure correspondences between induced attention distributions and coreference relations and observe that the model implicitly captures anaphora. It is consistent with gains for sentences where pronouns need to be gendered in translation. Beside improvements in anaphoric cases, the model also improves in overall BLEU, both over its context-agnostic version (+0.7) and over simple concatenation of the context and source sentences (+0.6).

Context-Aware Neural Machine Translation Learns Anaphora Resolution: An Expert Overview

The paper entitled "Context-Aware Neural Machine Translation Learns Anaphora Resolution" presents a significant advancement in neural machine translation (NMT) by proposing an innovative context-aware approach that incorporates extra-sentential information to enhance translation performance. The authors, Elena Voita et al., address a well-known limitation in conventional NMT systems, which typically process sentences in isolation, thereby neglecting crucial discourse phenomena essential for translation coherence and disambiguation.

Core Contributions

This research introduces a context-aware NMT model based on the Transformer architecture, which extends the capabilities of context-agnostic NMT by integrating contextual information from preceding sentences. The model employs a unique structure where a source sentence and its context are independently encoded, followed by a controlled information exchange through an attention layer. This mechanism effectively leverages contextual cues to improve pronoun translation, a notoriously challenging task in machine translation.

Key contributions of the paper include:

• A novel context-aware neural model with an interpretable interface for integrating contextual data into the translation process.
• Demonstrated improvements in translation quality, particularly in cases involving pronouns that require gender agreement in languages like Russian.
• Empirical analysis revealing the model's implicit learning of coreference and anaphora resolution, achieved without explicit feature engineering.

Methodology and Experiments

The paper uses the English-Russian OpenSubtitles dataset to evaluate the efficacy of the model. The authors highlight significant enhancements in BLEU scores, showing a gain of +0.7 compared to a context-agnostic baseline and +0.6 over a simplistic concatenation of context and source sentences. Notably, the model performs exceptionally well in translating pronouns such as "it," "you," and "I," especially when contextual information aids in resolving their referents' genders or numbers.

The model's architecture allows for a nuanced examination of attention mechanisms, showing increased attention to contextual information for certain ambiguous pronouns. This attention is interpreted as a latent form of anaphora resolution, effectively aligning with coreference systems despite not being explicitly programmed to do so.

Implications and Future Directions

The implications of this research are twofold:

1. Practical: The development of context-aware models that improve translation accuracy, particularly for languages requiring grammatical gender agreement, can significantly enhance the quality of machine translation systems in real-world applications.
2. Theoretical: By demonstrating that an NMT system can implicitly capture coreference phenomena, this paper opens up avenues for exploring other discourse elements, such as elliptical constructions and discourse cohesiveness, in machine translation.

Future work could explore refining the anaphora resolution capabilities of such models by integrating sophisticated features or leveraging latent relations between entities across broader contexts. Additionally, similar methodologies could be extended to other discourse phenomena, potentially revolutionizing the way context is handled in machine translation tasks.

Conclusion

In conclusion, the paper makes a compelling case for context-aware neural architectures in NMT, providing empirical evidence of their potential to tackle complex linguistic challenges, such as anaphora resolution, which traditional models struggle with. This work not only advances current understanding but also sets a solid groundwork for future investigations into discourse-aware LLMs.