Dissecting Contextual Word Embeddings: Architecture and Representation (1808.08949v2)

Abstract: Contextual word representations derived from pre-trained bidirectional LLMs (biLMs) have recently been shown to provide significant improvements to the state of the art for a wide range of NLP tasks. However, many questions remain as to how and why these models are so effective. In this paper, we present a detailed empirical study of how the choice of neural architecture (e.g. LSTM, CNN, or self attention) influences both end task accuracy and qualitative properties of the representations that are learned. We show there is a tradeoff between speed and accuracy, but all architectures learn high quality contextual representations that outperform word embeddings for four challenging NLP tasks. Additionally, all architectures learn representations that vary with network depth, from exclusively morphological based at the word embedding layer through local syntax based in the lower contextual layers to longer range semantics such coreference at the upper layers. Together, these results suggest that unsupervised biLMs, independent of architecture, are learning much more about the structure of language than previously appreciated.

Analyzing the Architecture and Representation of Contextual Word Embeddings

The paper "Dissecting Contextual Word Embeddings: Architecture and Representation" offers a comprehensive paper of contextual word representations derived from pre-trained bidirectional LLMs (biLMs). The authors focus on understanding how different neural architectures impact the learning and representation of contextual word embeddings, which have been shown to enhance various NLP tasks.

Summary of Findings

The paper presents an empirical investigation into how neural architectures—namely Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN), and Transformer models—affect the accuracy of NLP tasks and the properties of the learned word representations. A salient finding is the trade-off between accuracy and computational speed across these architectures. While LSTM-based models typically yield the highest accuracy, CNNs and Transformers exhibit enhanced computational efficiency, suggesting their suitability for large-scale applications.

The research reveals that, irrespective of the underlying architecture, all biLMs learn high-quality contextual embeddings that outperform traditional word embeddings such as GloVe in various challenging NLP tasks. Notably, these models encode a hierarchical structure of linguistic information, where lower layers focus on morphology and local syntactic features, and higher layers capture semantic relations, including coreferential links.

Experimental and Analytical Procedures

The authors explore four benchmark NLP tasks—multi-NLI, semantic role labeling (SRL), constituency parsing, and named entity recognition (NER)—demonstrating the adaptability of contextual embeddings across different paradigms. They utilize a common framework to replace pre-trained word vectors with contextual word vectors derived from each biLM, allowing for a direct comparison of their efficacy.

In addition to the comparative analysis, the paper explores the intrinsic properties of the contextual representations using various probing techniques. This includes examining intra-sentence contextual similarities, word-level syntactic and semantic analogies, and the influence of span representations. Employing linear probes, the authors systematically evaluate how well context layers encode syntactic and semantic information.

Quantitative Observations

• Performance Metrics: Across all NLP tasks, significant improvements are seen when contextual embeddings are used over GloVe vectors, with relative improvements in error reduction ranging from 13% to 25%.
• Hierarchical Representation: The models exhibit a robust hierarchy in terms of language understanding — morphological features are primarily encoded at the word embedding layers, while local syntactic and longer-range semantic features manifest at intermediate and higher layers, respectively.
• Coreference Resolution: Unsupervised tests demonstrate that higher network layers capture coreference information more effectively than lower layers, aligning with the observed results from supervised coreference models.

Implications and Future Directions

The paper underscores the potential of bidirectional LMs as general-purpose feature extractors in NLP, paving the way for vision-inspired feature reuse and transferability across tasks. Despite their success, these models still face challenges due to their dependence on word surface forms and sequential modeling, highlighting a potential area for future exploration—integrating explicit syntactic structures or linguistic inductive biases.

Another avenue for future research could involve scaling these architectures in terms of model size and data volume to further enhance embedding quality. Additionally, exploring avenues that fuse unsupervised biLM objectives with task-specific datasets via multitask or semi-supervised approaches may provide richer representations.

In conclusion, the paper offers valuable insights into the mechanics of contextual word embeddings and their architectural influences. By dissecting these advanced models, the authors contribute to a deeper understanding of how machines learn language structures, which is crucial for advancing NLP technologies.