Universal Sentence Encoder (1803.11175v2)

Abstract: We present models for encoding sentences into embedding vectors that specifically target transfer learning to other NLP tasks. The models are efficient and result in accurate performance on diverse transfer tasks. Two variants of the encoding models allow for trade-offs between accuracy and compute resources. For both variants, we investigate and report the relationship between model complexity, resource consumption, the availability of transfer task training data, and task performance. Comparisons are made with baselines that use word level transfer learning via pretrained word embeddings as well as baselines do not use any transfer learning. We find that transfer learning using sentence embeddings tends to outperform word level transfer. With transfer learning via sentence embeddings, we observe surprisingly good performance with minimal amounts of supervised training data for a transfer task. We obtain encouraging results on Word Embedding Association Tests (WEAT) targeted at detecting model bias. Our pre-trained sentence encoding models are made freely available for download and on TF Hub.

Universal Sentence Encoder: An Expert Overview

The paper "Universal Sentence Encoder" introduces two novel models for encoding sentences into embedding vectors aimed primarily at enhancing transfer learning capabilities across diverse NLP tasks. These models, developed by researchers at Google, emphasize efficiency and accuracy, and come in two variants designed to balance performance with computational resource constraints.

Introduction and Motivation

One of the significant challenges in NLP is the scarcity of large annotated datasets required for training sophisticated deep learning models. Traditional approaches have leveraged pre-trained word embeddings like word2vec and GloVe to perform limited transfer learning. However, recent advancements have hinted at the superior performance potential of sentence-level embeddings.

This paper proposes two universal sentence encoding models that can be effectively transferred to a myriad of NLP tasks with varying amounts of task-specific training data. The authors have made these models publicly accessible through TensorFlow Hub.

Encoder Architectures

Transformer-Based Encoder

The first model leverages the transformer architecture, specifically the encoding sub-graph, to generate context-aware word representations. These representations are further aggregated into fixed-length sentence embeddings. The transformer-based encoder optimizes for high accuracy, using a multi-task learning strategy across various tasks like Skip-Thought, conversation modeling, and classification to ensure a broad applicability.

Deep Averaging Network (DAN) Encoder

The second model is based on a Deep Averaging Network (DAN). Unlike the transformer, DAN generates sentence embeddings by averaging word and bigram embeddings, followed by passing them through a feedforward neural network. This approach sacrifices a bit of accuracy but is computationally efficient, as its complexity scales linearly with sentence length.

Training Data and Methodology

The sentence encoders were trained on an unsupervised corpus sourced from diverse web-based texts such as Wikipedia, news articles, and forum discussions. Additionally, supervised data from the Stanford Natural Language Inference (SNLI) corpus was used to enhance transfer performance. This mixed approach draws from the findings of previous studies indicating that such training regimes improve embedding quality.

Experimental Results

The authors conducted extensive experiments on several transfer tasks, summarized in Table 2, including:

• MR: Movie review sentiment analysis
• CR: Customer review sentiment analysis
• SUBJ: Subjectivity classification
• MPQA: Opinion polarity detection
• TREC: Question classification
• SST: Phrase-level sentiment classification
• STS Benchmark: Sentence similarity evaluation
• WEAT: Model bias detection

The results indicate that sentence-level transfer learning consistently outperforms word-level approaches in terms of accuracy across most tasks. Notably, models combining both word and sentence embeddings yielded the highest performance. The experiments also revealed that transfer learning is particularly beneficial when the available task-specific training data is limited, showcasing strong performance with minimal data.

Computational and Memory Efficiency

The paper provides a thorough analysis of the computational and memory efficiency of both models. The transformer model's time and space complexity scale quadratically, making it less efficient for longer sentences. In contrast, the DAN model's linear complexity ensures stable performance across sentence lengths, making it suitable for applications with limited computational resources.

Bias Evaluation

To address potential biases in the universal sentence encoders, the authors conducted Word Embedding Association Tests (WEAT). While the encoders showed associations consistent with human biases, such as the association of flowers and pleasantness, the extent of undesirable biases (e.g., racism, sexism) was weaker compared to previous models like GloVe.

Conclusion

The Universal Sentence Encoder models offer robust, scalable solutions for transfer learning in NLP. Both the transformer and DAN-based encoders facilitate high performance on various tasks, with the DAN model providing a more resource-efficient alternative. The availability of these models through TensorFlow Hub broadens their applicability for research and industry applications, promising advancements in the understanding and processing of natural language.

Future Implications

The research implies promising future directions for AI development. A key area of exploration could be further optimization of sentence encoders to balance accuracy and efficiency across even broader application contexts. Additionally, addressing inherent biases in embedding models will be crucial to making NLP systems fairer and more reliable. These developments underscore the potential for improving AI's capability to understand and generate human language efficiently and ethically.