Many Languages, One Parser (1602.01595v4)

Abstract: We train one multilingual model for dependency parsing and use it to parse sentences in several languages. The parsing model uses (i) multilingual word clusters and embeddings; (ii) token-level language information; and (iii) language-specific features (fine-grained POS tags). This input representation enables the parser not only to parse effectively in multiple languages, but also to generalize across languages based on linguistic universals and typological similarities, making it more effective to learn from limited annotations. Our parser's performance compares favorably to strong baselines in a range of data scenarios, including when the target language has a large treebank, a small treebank, or no treebank for training.

• The paper presents a unified parser, MaLOPa, that integrates language embeddings, joint POS tagging, and typological features to enhance multilingual syntactic analysis.
• It achieves competitive accuracy on high-resource languages and boosts performance in low-resource settings through indirect supervision.
• The parser effectively handles zero-resource scenarios by incorporating cross-lingual lexical knowledge, outperforming existing methods in labeled attachment scores.

Many Languages, One Parser: A Multilingual Dependency Parsing Approach

The paper "Many Languages, One Parser" presents a unified model for multilingual dependency parsing, capitalizing on recent advancements in NLP techniques. The authors propose a single, multilingual parsing architecture capable of parsing sentences across different languages by utilizing a blend of multilingual word clusters and embeddings, token-level language information, and language-specific features.

Overview

The presented model, abbreviated as MaLOPa, is trained on the union of treebanks from various languages, allowing it to parse effectively across numerous linguistic contexts. The input representation leverages universal dependency annotations, multilingual embeddings, typological information, and fine-grained POS tag embeddings. This comprehensive input system provides the parser with the capability to generalize syntactic structures using both linguistic universals and typological resemblances among languages.

Performance in Various Scenarios

One of the key strengths of MaLOPa is its adaptability across several data scenarios:

1. High-Resource Languages: The parser demonstrates competence in parsing languages with extensive treebank resources, maintaining competitive accuracy compared to language-specific models. The inclusion of language embeddings helps tailor the parser's behavior to the syntactic idiosyncrasies of each language, which is reflected in the accurate handling of attachments and long-distance dependencies.
2. Low-Resource Languages: When dealing with languages that possess limited annotated data, the parser utilizes multilingual resources for indirect supervision, significantly improving parsing performance. This functionality is crucial given the scarcity of linguistic resources for many natural languages.
3. Languages Without Treebanks: In zero-resource settings, the parser integrates cross-lingual lexical knowledge via embeddings and clusters to facilitate parsing, outperforming several existing approaches in terms of labeled attachment scores.

Methodological Contributions

The work introduces several methodological innovations:

• Language Embeddings: By incorporating language information through embeddings, the parser adapts to the input language, allowing for improved handling of language-specific syntactic structures.
• Joint POS Tagging and Parsing Model: The model predicts part-of-speech tags during parsing, employing a shared architecture that robustly manages instances of incorrect POS predictions, thereby enhancing parsing reliability.
• Unified Parsing Architecture: By collapsing separate, language-specific parsing models into a single architecture, MaLOPa simplifies the deployment and distribution of NLP tools across multiple languages, addressing both practical deployment concerns and theoretical exploration of linguistic universals.

Implications and Future Work

The implications of this research are particularly significant for expanding NLP capabilities to include under-resourced languages. The theoretical contribution of an adaptable, language-agnostic parsing model highlights potential for uncovering deeper syntactic patterns shared across language families.

Moving forward, the integration of unsupervised or semi-supervised techniques—potentially leveraging large-scale unannotated data—could further refine the parser's efficacy. Additionally, exploring synergies between multilingual embeddings and more sophisticated typological data could enrich the model's ability to parse highly diverse linguistic inputs.

By refining its approach to typological data integration and lexical embedding alignment, MaLOPa could pave the way for more nuanced models capable of capturing a broader range of linguistic phenomena, ultimately advancing the field of cross-lingual NLP.