DEPTH: Discourse Education through Pre-Training Hierarchically

Abstract: LLMs (LMs) struggle with linguistic understanding at the discourse level, even though discourse patterns such as coherence, cohesion, and narrative flow are prevalent in their pre-training data. To improve the discourse capabilities of LMs already at the pre-training stage, we introduce DEPTH, an encoder-decoder model that learns latent representations for sentences using a discourse-oriented pre-training objective. DEPTH combines hierarchical sentence representations with two objectives: (1) Sentence Un-Shuffling, and (2) Span-Corruption. Our approach trains the model to represent both sub-word-level and sentence-level dependencies over a pre-training corpora. When trained either from scratch or continuing from a pre-trained T5 checkpoint, DEPTH learns semantic and discourse-level representations faster than T5, outperforming it in span-corruption loss despite the additional sentence-un-shuffling objective. Evaluations on the GLUE, DiscoEval, and NI benchmarks demonstrate DEPTH's ability to quickly learn diverse downstream tasks, which require syntactic, semantic, and discourse capabilities. Our approach extends the discourse capabilities of T5, while minimally impacting other natural language understanding (NLU) capabilities in the resulting LM. We share our codebase for reproducibility: https://github.com/zbambergerNLP/depth.git.

• The paper introduces DEPTH, a model that uses hierarchical attention and dual pre-training objectives to capture both sub-word and sentence-level dependencies.
• DEPTH combines sentence un-shuffling with span corruption tasks, achieving rapid convergence and outperforming a T5 baseline on discourse-centric benchmarks.
• Its approach improves learning efficiency and scalability, offering practical benefits for applications such as dialogue systems, summarization, and general language understanding.

Analysis of DEPTH: Discourse Education through Pre-Training Hierarchically

The paper "DEPTH: Discourse Education through Pre-Training Hierarchically" presents an innovative approach aimed at enhancing the discourse capabilities of LMs. The authors introduce DEPTH, a model that integrates a discourse-oriented pre-training objective into the pre-training phase, distinctively optimizing both sub-word and sentence-level dependencies.

Methodological Advances

DEPTH represents a step forward in tackling the perennial challenge of capturing discourse-level understanding in LMs. This is achieved through two primary innovations:

1. Hierarchical Sentence Representations: DEPTH employs hierarchical attention mechanisms, allowing the model to learn complex interdependencies at both the sub-word and sentence levels. This architecture facilitates an understanding of coherence, cohesion, and narrative flow—critical aspects of textual discourse.
2. Dual Pre-Training Objectives: The model leverages a combination of Sentence Un-Shuffling and Span-Corruption objectives. The former involves tasking the model with reconstructing shuffled sentences, encouraging it to grasp the broader context, while the latter focuses on traditional span corruption, ensuring robust sub-word semantic representation.

Experimental Evaluation

The evaluation of DEPTH was thorough, with experiments encompassing both from-scratch (FS) and continuous pre-training (CPT) setups. The model was benchmarked against the T5 baseline, a well-established encoder-decoder architecture, on tasks extracted from the GLUE, DiscoEval, and NI benchmarks, which require varying degrees of syntactic, semantic, and discourse comprehension.

Notably, despite addressing a challenging additional pre-training objective, DEPTH demonstrated rapid convergence to lower validation loss levels compared to the T5 baseline in both FS and CPT paradigms. This efficiency extends to downstream task performance, where DEPTH exhibits proficiency, especially in discourse-centric benchmarks like DiscoEval, surpassing several state-of-the-art LMs in discourse coherence tasks.

Implications and Future Directions

DEPTH's approach to incorporating discourse comprehension directly into the pre-training phase has several implications:

• Practical Impact: The improved learning efficiency of DEPTH—even when initialized from scratch—suggests potential reductions in computational cost and time, compared to models that require extensive fine-tuning with annotated datasets.
• Broader Task Applicability: By refining discourse understanding, DEPTH not only advances performance on specific discourse tasks but also enhances general language understanding, potentially benefiting a range of applications including dialogue systems, summarization, and content generation.
• Scalability: An avenue for future research lies in scaling DEPTH's hierarchical architecture to accommodate longer textual inputs, leveraging its discourse-aware representations for tasks requiring the processing of extensive documents or books.

The authors' contribution of a pre-training objective that enriches hierarchical representation learning sets a promising path for future LMs, positing that the integration of multi-level discourse objectives is essential for advancing holistic natural language understanding. DEPTH's design and empirical results provide a framework that could inspire subsequent research into more nuanced and scalable language pre-training paradigms in AI.