Span-Aggregatable, Contextualized Word Embeddings for Effective Phrase Mining (2405.07263v1)

Abstract: Dense vector representations for sentences made significant progress in recent years as can be seen on sentence similarity tasks. Real-world phrase retrieval applications, on the other hand, still encounter challenges for effective use of dense representations. We show that when target phrases reside inside noisy context, representing the full sentence with a single dense vector, is not sufficient for effective phrase retrieval. We therefore look into the notion of representing multiple, sub-sentence, consecutive word spans, each with its own dense vector. We show that this technique is much more effective for phrase mining, yet requires considerable compute to obtain useful span representations. Accordingly, we make an argument for contextualized word/token embeddings that can be aggregated for arbitrary word spans while maintaining the span's semantic meaning. We introduce a modification to the common contrastive loss used for sentence embeddings that encourages word embeddings to have this property. To demonstrate the effect of this method we present a dataset based on the STS-B dataset with additional generated text, that requires finding the best matching paraphrase residing in a larger context and report the degree of similarity to the origin phrase. We demonstrate on this dataset, how our proposed method can achieve better results without significant increase to compute.

• The paper introduces SLICE, a novel method that aggregates word embeddings from text spans to enhance phrase mining in noisy contexts.
• It employs contrastive learning with a custom cross-entropy loss to optimize span-level representations over full-sentence approaches.
• Experiments using MS-MARCO and modified STS-B data show that SLICE significantly outperforms traditional methods in effective phrase retrieval.

Span-Aggregatable, Contextualized Word Embeddings for Effective Phrase Mining

Introduction

Phrase mining is crucial for various applications where we need to find relevant phrases within a large body of text. Think about analyzing conversations in call centers, parsing through legal documents, or even issue tracking systems. Traditional methods like BM25 and query expansion have been widely used, but recent advancements in NLP gave us Sentence-BERT, which provides improved dense representations for sentences. However, when target phrases are hidden within longer, noisy contexts, current approaches struggle to be effective. This research tackles that dilemma by suggesting a new method: Span-Aggregatable Contextualized Word Embeddings, or SLICE.

Key Concepts and Methodology

When dealing with real-world data, we often need to find a phrase that might be surrounded by lots of other text. Representing the entire sentence with a single dense vector simply isn't effective here. Instead, what the researchers propose is breaking down the sentences into smaller spans and then representing each of these spans with its own dense vector.

To do this efficiently, the researchers developed new training objectives that allow word embeddings to be dynamically aggregated into meaningful span embeddings. This approach hinges on two core ideas:

1. Contextualized Embeddings: Each word's embedding should reflect its meaning in the specific context it's in.
2. Span-Aggregatability: Mean-pooling word embeddings within a span should yield a vector that accurately captures the span's semantics.

Creating the SLICE Model

SLICE is built on a variation of contrastive learning. Normally, you might use Sentence-BERT's approach, where the goal is to push embeddings of similar sentences closer together and push dissimilar ones apart. SLICE alters this approach by ensuring that the embeddings remain meaningful when averaged over arbitrary spans.

How SLICE Works:

1. Training Data: Leveraging the MS-MARCO dataset, which, although noisy, contains pairs of queries and matching passages. Each passage has spans that are similar to the query.
2. Max-Pooling Spans: During training, the model finds the span in the target passage that best matches the query and adjusts embeddings to maximize this similarity.
3. Loss Function: The researchers employed a cross-entropy loss designed to maximize the similarity between the query and the best-matching span while minimizing similarity with non-matching spans.

Experimental Setup and Results

The researchers tested their model against several others, using a modified version of the STS-B dataset, which includes phrases embedded in noisy contexts. Here’s a breakdown of the methods compared:

1. Full Context: Representing the entire sentence as one dense vector.
2. N-grams - Forward per N-gram: Representing each span individually, leading to high compute costs.
3. N-grams - Single Forward Pass: Using mean-pooling on token embeddings generated from a single forward pass of the entire context. This is where SLICE shines.

Results, measured in terms of Pearson and Spearman correlation on challenges with noisy contexts, showed that SLICE outperformed other methods considerably, especially when accounting for compute requirements.

Key Findings:

• SLICE showed improvements over models designed for sentence-level tasks when applied to phrase mining.
• Representing span embeddings using contextualized word embeddings and mean-pooling resulted in better performance than direct full context or isolated span embeddings.

Practical Implications and Future Directions

The practical upshot of this research is evident for any system that needs to retrieve or identify relevant phrases within larger texts. Contact centers, legal document search tools, and similar applications can benefit significantly from more precise and contextually aware phrase retrieval.

Future Directions:

1. Training Data Exploration: Further investigations could look into the balance between training data and optimization objectives.
2. Optimized Span-Length Parameters: Adapting dynamically to different phrase lengths can potentially improve the performance even further.
3. Efficiency Improvements: Dealing with computational costs remains a key focus. Future models may look to streamline processing without compromising on performance.

Conclusion

This paper offers a substantial improvement over existing sentence-level methods for phrase mining. By emphasizing span-aggregatable, contextualized embeddings and optimizing training objectives, SLICE provides more accurate phrase representations within noisy contexts, paving the way for more effective real-world applications. While it’s just another step forward, it demonstrates that even dense vector representations can be wrangled to deliver precisely what complex queries need.