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Linguistic Knowledge Can Enhance Encoder-Decoder Models (If You Let It) (2402.17608v1)

Published 27 Feb 2024 in cs.CL

Abstract: In this paper, we explore the impact of augmenting pre-trained Encoder-Decoder models, specifically T5, with linguistic knowledge for the prediction of a target task. In particular, we investigate whether fine-tuning a T5 model on an intermediate task that predicts structural linguistic properties of sentences modifies its performance in the target task of predicting sentence-level complexity. Our study encompasses diverse experiments conducted on Italian and English datasets, employing both monolingual and multilingual T5 models at various sizes. Results obtained for both languages and in cross-lingual configurations show that linguistically motivated intermediate fine-tuning has generally a positive impact on target task performance, especially when applied to smaller models and in scenarios with limited data availability.

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Authors (3)
  1. Alessio Miaschi (10 papers)
  2. Felice Dell'Orletta (12 papers)
  3. Giulia Venturi (2 papers)

Summary

Enhancing Encoder-Decoder Models with Linguistic Knowledge for Sentence Complexity Prediction

Introduction

Recent advancements in NLP (Natural Language Processing) have leveraged the potent capabilities of pre-trained Neural LLMs (NLMs) across a myriad of tasks. An intriguing area of paper focuses on the interplay between these models and the incorporation of linguistic knowledge to bolster their performance on specific tasks. This paper explores this domain by fine-tuning T5, a renowned Encoder-Decoder model, using an intermediate task aimed at predicting structural linguistic properties of sentences. The ultimate goal is to assess the impact of this linguistically enriched fine-tuning on the model's ability to predict sentence-level complexity. The methodology and experiments extend across both Italian and English datasets, utilizing mono- and multi-lingual T5 models of varying sizes, thereby offering insights into the scalability and adaptability of this approach across languages and data scenarios.

Experimental Framework

The crux of the experimental design hinges on a two-phase STILTs (supervised training on intermediate labeled-data tasks) approach. Initially, T5 models undergo fine-tuning on a set of intermediate support tasks that encapsulate various linguistic phenomena identified as potential correlates of sentence complexity. This procedure, grounded in multi-task learning, generates linguistically informed T5 models—denoted as LiT5—each snapshot representing a distinct phase of linguistic knowledge acquisition. Following this, the LiT5 models are further fine-tuned on the task of predicting sentence complexity to ascertain the effectiveness of the linguistic knowledge gained in the initial phase.

Data for the target task and intermediate tasks were meticulously curated from Italian and English Universal Dependency Treebanks and evaluated across both languages using monolingual and multilingual T5 model configurations. The intermediate tasks focused on predicting a subset of linguistic features directly correlated with sentence complexity, as established through a rigorous selection process based on the correlation with complexity judgments.

Findings

The analyses reveal several notable insights:

  • Linguistically informed fine-tuning generally enhances model performance on the target task of predicting sentence complexity, particularly in models of smaller sizes and scenarios characterized by limited data availability.
  • The impact of model size on learning linguistic features is pronounced, with larger models showing superior performance but smaller models displaying significant improvement rates, indicating their capacity to substantially benefit from the addition of explicit linguistic knowledge.
  • The effectiveness of linguistically informed models transcends language barriers, as evidenced by the performance gains in cross-lingual evaluation settings, suggesting the robustness and adaptability of the methodology across languages.
  • Preliminary investigations into the influence of individual linguistic features highlight the multifaceted nature of linguistic complexity and underscore the potential of a multi-task learning framework to exploit the interdependencies among linguistic phenomena more effectively than focusing on single features.

Implications and Future Directions

This paper underscores the value of integrating linguistic knowledge into the fine-tuning process of pre-trained Encoder-Decoder models, such as T5, to enhance their performance on tasks reliant on understanding complex linguistic properties. The findings advocate for a nuanced approach to model training, emphasizing the utility of leveraging linguistic insights to inform model adjustments, particularly in data-constrained scenarios and for languages with varying degrees of representation in pre-training corpora.

Looking forward, the methodology presents a promising avenue for further exploration, particularly concerning its efficacy with large, generative LLMs in zero- and few-shot settings. The potential of instructional fine-tuning phases to impart linguistic knowledge that significantly boosts model performance in linguistically demanding tasks warrants in-depth investigation. Additionally, future work could expand the scope of languages and tasks, explore various model architectures and sizes, and refine the selection and utilization of linguistic features to fully capture their benefits.

The integration of linguistic knowledge into NLMs, as demonstrated in this paper, not only enhances model performance on specific tasks but also contributes to the broader understanding of how these models capture and utilize linguistic phenomena, offering valuable insights for the development of more efficient, adaptable, and linguistically competent NLMs.

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