Charagram: Embedding Words and Sentences via Character n-grams (1607.02789v1)

Abstract: We present Charagram embeddings, a simple approach for learning character-based compositional models to embed textual sequences. A word or sentence is represented using a character n-gram count vector, followed by a single nonlinear transformation to yield a low-dimensional embedding. We use three tasks for evaluation: word similarity, sentence similarity, and part-of-speech tagging. We demonstrate that Charagram embeddings outperform more complex architectures based on character-level recurrent and convolutional neural networks, achieving new state-of-the-art performance on several similarity tasks.

An Evaluation of charagram: Character $n$-gram-Based Text Embeddings

The presented paper introduces "charagram," a novel approach for embedding textual sequences using character $n$-grams, a method that simplifies the representation of words and sentences. Character embeddings are pivotal for natural language understanding, given their potential to capture subword information beneficial for handling rare words and morphological variants. This paper systematically evaluates the charagram methodology against existing architectures, including recurrent (RNN) and convolutional neural networks (CNN), across tasks focused on word similarity, sentence similarity, and part-of-speech tagging.

Methodology and Experimentation

Charagram embeddings derive from vectors of character $n$-gram counts transformed through a nonlinear mapping into a low-dimensional space. This approach aligns with the principles underlying the Deep Semantic Similarity Model (DSSM) but extends its application to both words and sentences with broader experimental setups.

The authors compare charagram with established character-based models, specifically charLSTM (LSTM over characters) and charCNN (CNN with character $n$-gram filters). Experiments were conducted using datasets such as WordSim-353, SimLex-999 for word similarity, and an extensive range of sentence similarity datasets from past SemEval STS challenges.

Results and Findings

Quantitative assessments reveal that charagram consistently outperforms charLSTM and charCNN architectures, notably achieving state-of-the-art results on the SimLex-999 dataset. In sentence similarity tasks, charagram is competitive, outperforming other models on many datasets. Importantly, while the gap in part-of-speech tagging performance was marginal across models, charagram exhibited rapid convergence to high performance, emphasizing its computational efficiency when compared to more complex architectures.

A significant advantage of charagram is its robustness to out-of-vocabulary words, an acknowledged limitation in previous models such as paragram-phrase. The inherent capability to embed any character sequence rather than predefined words permits charagram to handle rare or morphologically complex words effectively.

The paper also explores the trade-offs between model complexity and performance, finding that the semantic richness captured by charagram can be amplified with extended character $n$-gram vocabularies, though satisfactory results are achievable even with smaller subsets of $n$-grams.

Implications and Future Directions

The charagram approach's implications are profound for NLP, where computational efficiency and accuracy are both critical. By eschewing complex neural architectures in favor of a streamlined, character $n$-gram-based approach, charagram lays a foundational perspective for future text embedding innovations. Specifically, enhancing word and sentence embeddings without the computational overhead associated with deep neural models presents an attractive path forward.

Further research could extend the principles of charagram embeddings to multilingual and domain-specific corpora, examining the transferability of learned embeddings across various linguistic or professional domains. Moreover, the character $n$-gram method opens opportunities for hybrid models that might integrate semantic knowledge representation with the simplicity of charagram embeddings, potentially improving contextual embeddings for complex textual analyses.

In summary, this paper provides a credible exploration of character $n$-gram embeddings, demonstrating their potential as efficient and effective components for driving advancements in NLP applications. The methodology and insights offered serve as a vital reference point for researchers and practitioners focused on optimizing text representation.