Semantic Specialisation of Distributional Word Vector Spaces using Monolingual and Cross-Lingual Constraints (1706.00374v1)

Abstract: We present Attract-Repel, an algorithm for improving the semantic quality of word vectors by injecting constraints extracted from lexical resources. Attract-Repel facilitates the use of constraints from mono- and cross-lingual resources, yielding semantically specialised cross-lingual vector spaces. Our evaluation shows that the method can make use of existing cross-lingual lexicons to construct high-quality vector spaces for a plethora of different languages, facilitating semantic transfer from high- to lower-resource ones. The effectiveness of our approach is demonstrated with state-of-the-art results on semantic similarity datasets in six languages. We next show that Attract-Repel-specialised vectors boost performance in the downstream task of dialogue state tracking (DST) across multiple languages. Finally, we show that cross-lingual vector spaces produced by our algorithm facilitate the training of multilingual DST models, which brings further performance improvements.

• The paper introduces the Attract-Repel algorithm that refines word vectors through synonym and antonym constraints from lexical resources.
• It employs mini-batch training and L2 regularization to update vectors context-sensitively, achieving state-of-the-art results on semantic similarity tasks.
• The approach improves multilingual dialogue state tracking by transferring semantic enhancements to low-resource languages, raising joint goal accuracy.

Semantic Specialisation of Distributional Word Vector Spaces using Monolingual and Cross-Lingual Constraints

The paper presents "Attract-Repel," an algorithm designed to enhance the semantic quality of word vectors by integrating constraints derived from lexical resources. This algorithm leverages both monolingual and cross-lingual resources, thereby enhancing the semantic specialization of cross-lingual vector spaces. It showcases its effectiveness through state-of-the-art results on semantic similarity datasets in multiple languages and demonstrates improvements in dialogue state tracking (DST) across multilingual settings.

Attract-Repel Algorithm

The Attract-Repel algorithm focuses on refining word vectors by utilizing synonymy and antonymy constraints sourced from lexical databases. It uniquely centers on operating over mini-batches of example pairs in the training phase, updating word vectors based on their relation to negative examples, ensuring context-sensitive alterations. This innovative approach enables Attract-Repel to outperform preceding methods like counter-fitting by introducing fine-grained vector updates and L2 regularization, crucially considering vectors' relationships within larger contextual cohorts.

Cross-Lingual Specialization

Central to Attract-Repel is its capability to utilize cross-lingual lexical resources, such as BabelNet, to inject inter-language constraints which successfully align and embed multiple languages into a unified vector space. This cross-lingual specialization facilitates effective semantic information transfer from resource-rich languages to those with fewer resources, thus significantly improving the semantic content of the vector spaces, evidenced in notable gains in languages of varying resource abundance. This advantage is notably demonstrated with Hebrew and Croatian, where performance on intrinsic evaluation tasks improved markedly.

Practical Applications and Performance

Critically, the paper extends the evaluation of Attract-Repel by testing its utility in practical NLP applications, specifically Dialogue State Tracking (DST). By enhancing vector semantic quality, Attract-Repel improves the understanding of user goals in dialog systems across languages, surpassing conventional methods in performance, especially in multilingual environments where it enables the training of a singular, effective DST model across multiple languages.

In particular, the paper highlights how the semantic specialisation of vectors, both mono- and cross-lingual, yields high-quality semantic spaces which support this application, with marked improvements in joint goal accuracy metrics for the DST task across language-specific and multilingual datasets.

Implications and Future Directions

Attract-Repel holds significant implications for the development and deployment of multilingual NLP systems. Its method of integrating linguistic constraints for word vector enhancement presents a robust avenue for achieving superior semantic embeddings, pivotal for applications in lower-resourced languages. Additionally, its ability to seamlessly integrate vocabulary from different languages into a coherent semantic clustering offers substantial potential for further explorations in multilingual AI systems.

Future work could strive to refine and extend the algorithm's capabilities, particularly exploring its application in morphologically rich languages and its adaptability to other NLP tasks requiring semantic precision. Additionally, researcher attention could focus on investigating the apparent incongruences between intrinsic evaluations of semantic vectors and downstream task performance to develop more comprehensive evaluation metrics for NLP models.

By bridging the gap between high-quality semantic vectors and improved task performance, Attract-Repel indeed provides a significant contribution to the computational linguistics domain, promising to foster enhancements in semantic modeling and language understanding across diverse linguistic landscapes.