Efficient Discourse Connective Detection Using Lightweight Gradient Boosting Model

Core Concepts

A lightweight, gradient-boosting-based system for detecting discourse connectives that achieves competitive performance with significantly faster inference speeds compared to deep learning-based alternatives.

Abstract

The authors introduce a lightweight discourse connective detection system that employs gradient boosting trained on straightforward, low-complexity features. This approach sidesteps the computational demands of current deep neural network-based approaches.
The key highlights of the proposed model are:

It achieves results close to state-of-the-art models while offering significant gains in terms of inference time, even on CPU.
The stable performance across two unrelated languages (English and Turkish) suggests the robustness of the system in a multilingual scenario.
The model is designed to support the annotation of discourse relations, particularly in scenarios with limited resources, while minimizing performance loss.

The authors train and evaluate their model on the PDTB 2.0 (English) and TDB 1.0 (Turkish) datasets. They show that their lightweight model outperforms a feature-based baseline and is at least three times faster than a BERT-based model, even when running on a CPU.
The feature importance analysis reveals that verb-based features are the most important aspects of the lightweight connective detection model. The authors argue that their approach, in addition to being lightweight compared to deep learning models, is also lightweight in terms of producing features effectively and at low cost.

Stats

The proposed model achieves F-scores of 83.58% on PDTB 2.0 and 78.94% on TDB 1.0, which are competitive with state-of-the-art deep learning-based models.
The inference time of the proposed model is at least 3 times faster than a BERT-based baseline, even when running on a CPU.
When both models are run on a GPU, the difference in inference time becomes nearly 250 times.

Quotes

"Despite our model's simplicity and reduced complexity, it demonstrates competitive performance when compared against the strong baselines."
"Our approach demonstrated robustness across English and Turkish, indicating its utility in multilingual settings and scenarios with limited computational resources."
"Thanks to the speed and accuracy of our system, our model can be used to mine large amounts of data that can be used to facilitate the development of new discourse-annotated corpora or as the training data of discourse-focused language models."

Key Insights Distilled From

Lightweight Connective Detection Using Gradient Boosting

by Must... at arxiv.org 04-23-2024

https://arxiv.org/pdf/2404.13793.pdf

Deeper Inquiries

How can the proposed lightweight model be further improved to achieve even higher performance, especially on the more challenging connectives?

To enhance the performance of the lightweight model on challenging connectives, several strategies can be implemented:

Fine-tuning on Challenging Connectives: The model can be fine-tuned specifically on a subset of data that contains more instances of challenging connectives. By focusing on these cases during training, the model can learn to better distinguish between different types of connectives.

Feature Engineering: Introducing more sophisticated linguistic features that capture the nuances of phrasal connectives can improve the model's performance. Features such as syntactic dependencies, semantic roles, or discourse relations between clauses could provide valuable information for identifying complex connectives.

Ensemble Methods: Combining the lightweight model with other models or ensembles of models can help capture diverse patterns in connective usage. By leveraging the strengths of different models, the ensemble approach can lead to improved performance on challenging cases.

Data Augmentation: Generating synthetic data or augmenting the existing dataset with variations of challenging connectives can expose the model to a wider range of examples, thereby improving its ability to generalize to complex cases.

Attention Mechanisms: Incorporating attention mechanisms into the model architecture can allow the model to focus on relevant parts of the input sequence when identifying connectives. This can help the model better handle long-range dependencies and complex structures in phrasal connectives.

What other linguistic features or techniques could be explored to enhance the model's ability to handle phrasal connectives more accurately?

To enhance the model's ability to accurately handle phrasal connectives, the following linguistic features and techniques could be explored:

Syntactic Parsing: Leveraging syntactic parsing techniques to extract syntactic structures such as constituent trees or dependency parses can provide valuable information about the relationships between words in phrasal connectives. These structures can help the model better understand the hierarchical nature of phrasal connectives.

Semantic Role Labeling: Incorporating semantic role labeling information can help the model identify the roles of different words within a phrasal connective. By understanding the semantic relationships between words, the model can more accurately detect the boundaries and functions of phrasal connectives.

Lexical Semantics: Utilizing information about the semantic properties of words in a phrasal connective can improve the model's ability to distinguish between different types of connectives. Features such as word embeddings or lexical resources can capture the nuanced meanings of words within phrasal connectives.

Discourse Structure Analysis: Considering the broader discourse context in which phrasal connectives occur can aid in their accurate identification. Features related to discourse relations, coherence relations, or discourse markers can provide valuable cues for detecting phrasal connectives.

Multi-task Learning: Training the model on related tasks such as discourse parsing, coreference resolution, or semantic parsing can help improve its understanding of phrasal connectives within the larger context of discourse processing. By jointly learning multiple tasks, the model can benefit from shared representations and improved generalization.

Given the model's efficiency, how could it be integrated into larger NLP pipelines or applications to enable more effective discourse-level processing?

The efficient lightweight model can be seamlessly integrated into larger NLP pipelines or applications to enhance discourse-level processing in the following ways:

Preprocessing Module: Incorporate the lightweight model as part of a preprocessing module to identify discourse connectives in the input text. This can help segment the text into coherent units and facilitate downstream processing tasks.

Feature Extraction: Use the model to extract linguistic features related to discourse connectives, which can then be utilized by higher-level NLP components such as sentiment analysis, summarization, or question-answering systems. These features can provide valuable insights into the discourse structure of the text.

Discourse Parsing: Integrate the model into a discourse parsing pipeline to automatically annotate discourse relations and discourse connectives in text. This can aid in the analysis of text coherence, argumentation structure, and rhetorical patterns.

Interactive Applications: Deploy the model in interactive applications such as chatbots or virtual assistants to enhance their ability to understand and generate coherent responses in natural language. By incorporating discourse-level processing, these applications can engage users more effectively in conversations.

Cross-lingual Applications: Extend the model's capabilities to handle multiple languages and integrate it into cross-lingual NLP pipelines. This can enable discourse-level processing in multilingual settings and support applications that require language-agnostic discourse analysis.

By integrating the lightweight model into larger NLP pipelines and applications, it can contribute to more effective discourse-level processing, enabling a wide range of text understanding tasks with improved efficiency and accuracy.