Multi-Task Cross-Lingual Sequence Tagging from Scratch (1603.06270v2)

Abstract: We present a deep hierarchical recurrent neural network for sequence tagging. Given a sequence of words, our model employs deep gated recurrent units on both character and word levels to encode morphology and context information, and applies a conditional random field layer to predict the tags. Our model is task independent, language independent, and feature engineering free. We further extend our model to multi-task and cross-lingual joint training by sharing the architecture and parameters. Our model achieves state-of-the-art results in multiple languages on several benchmark tasks including POS tagging, chunking, and NER. We also demonstrate that multi-task and cross-lingual joint training can improve the performance in various cases.

• The paper presents a novel hierarchical GRU model with CRF, eliminating feature engineering for robust sequence tagging.
• It leverages multi-task and cross-lingual training to share language-independent features and boost performance in low-resource scenarios.
• Empirical results demonstrate state-of-the-art F1 and accuracy scores on CoNLL and Penn Treebank benchmarks.

Insights into Multi-Task Cross-Lingual Sequence Tagging from Scratch

The paper "Multi-Task Cross-Lingual Sequence Tagging from Scratch" by Yang, Salakhutdinov, and Cohen presents an advanced approach to sequence tagging through a hierarchical recurrent neural network (RNN) employing gated recurrent units (GRUs) at both the character and word levels. This work stands out for its language and task independence, dispensing with traditional feature engineering, and its capacity to significantly improve performance through multi-task and cross-lingual joint training.

Sequence tagging, the process of labeling each word in a sequence with a corresponding tag (e.g., Part-Of-Speech (POS) tags, chunking, Named Entity Recognition (NER)), remains a prominent challenge in NLP. The innovation introduced in this paper lies in the robust application of GRUs combined with a Conditional Random Field (CRF) layer, implemented to model the structured prediction of sequences efficiently.

Technical Framework

The proposed model comprises a two-tier GRU setup:

• Character-Level GRU: Processes sequences of characters for each word to encapsulate morphological information.
• Word-Level GRU: Engages with the character-level outputs to capture contextual word information.

This hierarchical model optimizes its performance through a CRF layer that accentuates dependencies between tags, effectively considering the sequence as a structured entity.

Multi-Task and Cross-Lingual Joint Training

A distinctive feature of this work is its exploration into joint training schemes. The authors leverage two joint training methodologies:

1. Multi-Task Joint Training, which shares underlying parameters across diverse tasks within a single language, advocating for shared learning of language-specific properties.
2. Cross-Lingual Joint Training, emphasizing shared morphological traits across languages without reliance on parallel corpora.

The architecture supports these joint training paradigms by sharing GRU layers' parameters as appropriate, demonstrably improving task performance, particularly in low-resource settings.

Empirical Results

Experimental evaluation demonstrated noteworthy performance across multiple benchmarks:

• CoNLL 2000 Chunking: The model achieved a state-of-the-art F1 score of 95.41%.
• CoNLL 2002/2003 NER Tasks: State-of-the-art results were observed for Dutch NER with an F1 score of 85.19%, Spanish NER at 85.77%, and competitive results for English NER at 91.20%.
• Penn Treebank POS Tagging: Yielding an accuracy rate of 97.55%, aligning with prior top results.

The results affirm the efficacy of character-level GRU components in featuring both morphological and lexical-semantic aspects when coupled with proven GRU applications at the word level.

Implications and Future Directions

This research potentially transforms how sequence tagging frameworks are designed, particularly emphasizing resource optimization when training across diverse tasks and languages simultaneously. Future developments might expect enhanced cross-lingual integration, increasing model translatability across languages using semantic information derived from parallel corpora. Also, extending this work to address severely resource-constrained languages will validate the robustness and adaptability of the proposed joint training methodologies.

Concluding, this paper provides a sophisticated methodological advance in sequence tagging via deep learning. It articulates important pathways for further research and potential real-world applications harnessing joint-training strategies in NLP systems.