Learning General Purpose Distributed Sentence Representations via Large Scale Multi-task Learning (1804.00079v1)

Abstract: A lot of the recent success in NLP has been driven by distributed vector representations of words trained on large amounts of text in an unsupervised manner. These representations are typically used as general purpose features for words across a range of NLP problems. However, extending this success to learning representations of sequences of words, such as sentences, remains an open problem. Recent work has explored unsupervised as well as supervised learning techniques with different training objectives to learn general purpose fixed-length sentence representations. In this work, we present a simple, effective multi-task learning framework for sentence representations that combines the inductive biases of diverse training objectives in a single model. We train this model on several data sources with multiple training objectives on over 100 million sentences. Extensive experiments demonstrate that sharing a single recurrent sentence encoder across weakly related tasks leads to consistent improvements over previous methods. We present substantial improvements in the context of transfer learning and low-resource settings using our learned general-purpose representations.

Analysis of "Learning General Purpose Distributed Sentence Representations via Large Scale Multi-task Learning"

This paper presents a novel approach to learning fixed-length sentence representations through a multi-task learning framework focused on NLP. The authors address the challenge of extending successful word representation techniques to sequences of words, such as sentences, by leveraging multiple training objectives to create a versatile sentence encoder.

Model Architecture and Training Approach

The core contribution of this paper is the integration of diverse training signals within a single recurrent neural network (RNN) encoder, sharing it across various NLP tasks. By coupling multiple objectives into a cohesive model, this architecture harnesses the benefits of inductive biases from weakly related tasks. Specifically, the authors train a sequence-to-sequence model using a substantial dataset of over 100 million sentences from different sources, implementing multilingual neural machine translation (NMT), natural language inference, constituency parsing, and skip-thought vectors. The shared encoder is designed with a GRU architecture to efficiently manage these tasks, deferring task-specific variations to individual decoders.

Results and Findings

The empirical evaluations conducted suggest that the proposed multi-task model significantly outperforms previous methods in several NLP tasks, especially in low-resource settings and transfer learning scenarios. The robust experimentation indicates enhanced performance in sentiment classification, paraphrase identification, and sentence relatedness benchmarks. The authors report that even in severely reduced labeling scenarios (using only 6% of the training data for some datasets), their approach remains competitive with models trained from scratch.

Numerical Benchmarks

• The most notable improvements are observed in text classification tasks: 1.1%-2.0% gain from similar baselines.
• The model achieves a 6% increase in accuracy over prior methods on the TREC dataset, demonstrating its ability to generalize across various language processing tasks.
• For the task of paraphrase recognition, the method closes the performance gap with traditionally supervised models trained with complete datasets.

Theoretical and Practical Implications

Theoretically, the authors propose that their model capitalizes on inductive biases across a spectrum of tasks to devise a general-purpose sentence representation capable of adapting to new, unseen tasks. Practically, this research underscores the potential of multi-task learning frameworks to circumvent the dependency on large labelled datasets, offering an effective strategy for incorporating diverse linguistic structures and semantic information into fixed-length vectors.

Future Directions in AI

Future investigations could further explore the interpretability of the learned sentence embeddings, providing insights into which task-specific signals most effectively contribute to generalized language understanding. Additionally, the integration with techniques for generative LLMs may yield advancements in tasks involving text synthesis and conversational AI.

In conclusion, this paper sets the stage for a versatile approach in sentence embeddings, advocating for the broader usage of multi-task learning to substantially boost performance in low-resource environments and within diverse linguistic challenges. Such models promise significant advancements in automating the understanding and generation of human languages using AI methodologies.