Learning Joint Multilingual Sentence Representations with Neural Machine Translation (1704.04154v2)

Abstract: In this paper, we use the framework of neural machine translation to learn joint sentence representations across six very different languages. Our aim is that a representation which is independent of the language, is likely to capture the underlying semantics. We define a new cross-lingual similarity measure, compare up to 1.4M sentence representations and study the characteristics of close sentences. We provide experimental evidence that sentences that are close in embedding space are indeed semantically highly related, but often have quite different structure and syntax. These relations also hold when comparing sentences in different languages.

Overview of Multilingual Sentence Representation Learning through NMT

The paper "Learning Joint Multilingual Sentence Representations with Neural Machine Translation" by Holger Schwenk and Matthijs Douze investigates the capability of neural machine translation (NMT) frameworks to learn language-independent sentence representations. The researchers propose utilizing the encoder-decoder architecture typical in NMT applications to generate joint multilingual sentence embeddings. The objective is to encode sentences across different languages into a common semantic space, thereby facilitating multilingual NLP tasks such as translation, classification, and sentiment analysis.

Key Contributions

1. Multilingual Sentence Representation: The paper introduces a method to learn fixed-size sentence embeddings applicable across multiple languages. This approach aims to capture the semantic content of sentences regardless of linguistic variations, potentially serving as a continuous space interlingua.
2. NMT Framework with Multiple Encoders/Decoders: Employing NMT enabled the handling of sentences in six languages, utilizing dedicated encoders and decoders for each language. This configuration seeks to unify sentence representations by weakening the dependency on specific language structures during training.
3. Training Strategies: Experimentation with various partial training paths – such as one-to-one, one-to-many, and many-to-one – allowed the exploration of the effectiveness of different input-output combinations in fostering multilingual sentence similarity.
4. Evaluation Protocol: The research employs a novel evaluation framework based on multilingual similarity search that scales well with larger datasets and multiple languages. This approach measures the effectiveness of sentence representations by assessing cross-language sentence retrieval accuracy.

Evaluation and Findings

Through extensive experiments utilizing the UN parallel corpus covering six languages (English, French, Spanish, Russian, Arabic, and Chinese) alongside Europarl data for three languages, the models achieved promising results. Notably, the use of bidirectional LSTMs (BLSTMs) with max-pooling markedly improved the quality of multilingual embeddings. The system was able to achieve a similarity error rate as low as 1.2% across all 21 bilingual pair configurations.

Implications in NLP

The implications of this paper are profound for multilingual NLP applications. A universal sentence encoder that is language-agnostic and semantically robust could streamline processes like cross-lingual information retrieval, sentiment analysis, and robust translation systems. The method also hints at possible enhancements in zero-resource learning environments, offering a framework where new languages could be integrated into existing models with minimal additional data.

Future Directions

Potential future developments include exploring more sophisticated combination techniques for integrating sentence representations across languages and modalities. The incorporation of more advanced attention mechanisms tailored for sentence representation, rather than translation accuracy, also presents opportunities for further performance refinement. Moreover, extending the architecture to include languages beyond those in existing multilingual corpora could enhance the global coverage and efficacy of the proposed representations.

In conclusion, the paper successfully demonstrates that NMT frameworks can be leveraged to achieve joint sentence representations that are effective across multiple languages, offering valuable insights and tools for advancing multilingual NLP tasks.