Siamese CBOW: Optimizing Word Embeddings for Sentence Representations (1606.04640v1)

Abstract: We present the Siamese Continuous Bag of Words (Siamese CBOW) model, a neural network for efficient estimation of high-quality sentence embeddings. Averaging the embeddings of words in a sentence has proven to be a surprisingly successful and efficient way of obtaining sentence embeddings. However, word embeddings trained with the methods currently available are not optimized for the task of sentence representation, and, thus, likely to be suboptimal. Siamese CBOW handles this problem by training word embeddings directly for the purpose of being averaged. The underlying neural network learns word embeddings by predicting, from a sentence representation, its surrounding sentences. We show the robustness of the Siamese CBOW model by evaluating it on 20 datasets stemming from a wide variety of sources.

• The paper introduces the Siamese CBOW model to directly optimize word embeddings for enhanced sentence representations.
• It employs a cosine similarity-based softmax and cross-entropy loss to predict adjacent sentences using unlabeled text.
• The approach outperformed baseline models on 14 of 20 SemEval datasets, demonstrating its robust performance across diverse domains.

An Analytical Overview of the Siamese CBOW Model for Optimizing Word Embeddings in Sentence Representations

The paper, "Siamese CBOW: Optimizing Word Embeddings for Sentence Representations," introduces the Siamese Continuous Bag of Words (Siamese CBOW) model, an innovative neural network designed to improve sentence representations through optimized word embeddings. The paper argued that existing word embedding techniques fall short when it comes to effectively representing sentences. The researchers aimed to develop a model that could directly optimize embeddings suited for sentence-level tasks by operating efficiently on unlabeled data.

Overview of the Siamese CBOW Model

The Siamese CBOW model is rooted in the principle of averaging word embeddings to derive sentence embeddings, a method noted for its simplicity yet surprising effectiveness across a variety of tasks. Unlike traditional word embeddings, which are generally task-agnostic, Siamese CBOW emphasizes optimizing word embeddings specifically for the derivation of sentence vectors—a purposeful shift to enhance semantic comprehension at the sentence level.

The model leverages a novel approach where word embeddings are trained by predicting adjacent sentences from a given sentence representation. This strategy draws inspiration from methodologies like the Skip-Thought model but distinguishes itself by offering a more targeted optimization for sentence representation tasks.

Training and Network Architecture

The architecture involves leveraging a softmax mechanism, where the probability of sentence adjacency is computed using cosine similarity amongst sentence embeddings. The training phase focuses on minimizing a cross-entropy loss that hinges on correct predictions of sentence adjacency—thus harnessing large volumes of unlabeled text efficiently.

The model's training is iteratively performed, where the system adjusts its word embedding weights to better capture semantic proximity in sentences. Practically, this alteration results in embeddings where sentences with similar meanings have closer vector representations.

Experimental Evaluation

Siamese CBOW was rigorously tested across 20 SemEval datasets, encompassing varied textual sources such as newswire, video descriptions, and microblogs. The choice and diversity of these datasets underscore the authors' intent to demonstrate the robustness and domain-spanning effectiveness of their model.

Numerically, the model showed superior performance in 14 out of 20 instances when benchmarked against contemporary baseline models like word2vec (both Skipgram and CBOW) and skip-thought vectors. Particularly remarkable performances were observed in domains with higher semantic complexities, where establishing sentence-level coherence demands more than mere lexical similarity.

Implications and Further Considerations

The Siamese CBOW's impact stretches beyond the immediate context of the experiments. By showing enhanced performance on unsupervised tasks, this model sets a precedent for future developments in embedding strategies that can seamlessly transition between lexical and sentence-level understanding without drastic performance drops across varied domains. Furthermore, the model’s architectures and cost-efficient computation present practical applications for scaling sentence embedding generation in real-world scenarios, for instance, within search engines and sentiment analysis systems.

Future research might focus on extending the Siamese CBOW approach to larger text structures like paragraphs or documents and exploring its utility in supervised learning settings. Another avenue for development could be refining the model’s architecture, notably involving deeper networks which might further improve semantic capture without compromising computational efficiency.

The paper encapsulates a significant step towards more refined and purpose-built embeddings for sentence representation, positioning the Siamese CBOW as a valuable model for both academic exploration and practical applications in NLP.