Do Multi-Sense Embeddings Improve Natural Language Understanding? (1506.01070v3)

Abstract: Learning a distinct representation for each sense of an ambiguous word could lead to more powerful and fine-grained models of vector-space representations. Yet while `multi-sense' methods have been proposed and tested on artificial word-similarity tasks, we don't know if they improve real natural language understanding tasks. In this paper we introduce a multi-sense embedding model based on Chinese Restaurant Processes that achieves state of the art performance on matching human word similarity judgments, and propose a pipelined architecture for incorporating multi-sense embeddings into language understanding. We then test the performance of our model on part-of-speech tagging, named entity recognition, sentiment analysis, semantic relation identification and semantic relatedness, controlling for embedding dimensionality. We find that multi-sense embeddings do improve performance on some tasks (part-of-speech tagging, semantic relation identification, semantic relatedness) but not on others (named entity recognition, various forms of sentiment analysis). We discuss how these differences may be caused by the different role of word sense information in each of the tasks. The results highlight the importance of testing embedding models in real applications.

Insights into the Multi-Sense Embedding Model and Its Applications in NLP Tasks

The paper "Do Multi-Sense Embeddings Improve Natural Language Understanding?" by Jiwei Li and Dan Jurafsky investigates whether multi-sense embeddings contribute to enhancing NLP tasks by departing from traditional single embedding approaches. By leveraging Chinese Restaurant Processes (CRP) for effective sense induction and sense-specific embedding learning, the paper aims to uncover the potential benefits of adopting multi-sense embeddings across multiple NLP tasks.

Multi-Sense Embedding Framework

Multi-sense embeddings attempt to address the shortfall present in conventional word embedding models, where each word is represented with a single vector. The paper argues that such models inadequately capture the myriad senses associated with polysemous words, leading to imprecise or conflated semantic representations.

The authors propose a multi-sense embedding model that employs CRPs to dynamically induce and represent various senses of a word based on contextual cues. The CRP framework allows the model to allocate new vectors for words contingent on contextual evidence, enabling precise semantic disambiguation. This method places multivariate sense-induced vectors in the semantic space, resulting in granular and natural representations amenable to complex linguistic tasks.

Evaluation on NLP Tasks

To investigate the contribution of multi-sense embeddings in NLP, the authors applied this model to several tasks: part-of-speech tagging, named entity recognition (NER), sentiment analysis, semantic relation identification, and semantic relatedness. Outcomes from these applications are evaluated by comparing models utilizing single embeddings against those employing the proposed multi-sense embeddings.

1. Semantic Relatedness and Semantic Relationship Identification: Multi-sense embeddings demonstrate a significant advantage over single embeddings in these tasks. This is sensible as both tasks rely heavily on understanding nuanced semantics, which benefit from clearer sense distinctions afforded by multi-sense embeddings.
2. Part-of-Speech Tagging: Another area where multi-sense embeddings excel, as it seems closely aligned with explicit word-sense distinctions. The paper indicates notable performance improvements over single embedding models.
3. Named Entity Recognition: The benefits of multi-sense embeddings are less pronounced here. Given that many named entities are inherently distinct from sense-based representations, the marginal improvement aligns with expectations that specific tasks may not extract full potential from sense differentiation.
4. Sentiment Analysis: The findings reveal negligible advantages for sentiment analysis tasks using multi-sense embeddings. The reasoning offered is that sentiment mainly involves detecting polarity cues that are unimpacted by sense ambiguity, an expected outcome given the task's reliance on specific sentiment-laden features.

Challenges and Limitations

The paper underscores several methodological challenges inherent in adopting a multi-sense framework:

• Dimensionality Complexity: While multi-sense embeddings enhance performance in some tasks, increasing embedding dimensionality of single models could achieve similar enhancements.
• Impact of Model Complexity: The introduction of complex models, such as LSTMs, appears to attenuate the observed benefits of multi-sense embeddings, suggesting sophisticated architectures may mitigate the requirement for explicit sense disambiguation.

Implications and Future Considerations

Overall, the findings point towards the nuanced benefit of multi-sense embeddings, contingent on task specificity and the surrounding model architecture. This leads to a broader realization: more considerable gains could be made by strategically pairing embedding strategies with task-specific architectures.

Future explorations could focus on developing more robust sense induction algorithms that reduce errors during selection or on constructing hybrid models that leverage expressive simple embeddings alongside advanced context-aware models, potentially maximizing semantic fidelity without unduly increasing computational complexity. Additionally, further exploration into the interaction of multi-sense embeddings with powerful downstream models might illuminate novel aspects of information parsing and representation in machine learning.