Revisiting Topic-Guided Language Models

In recent years, language models (LMs) that use deep learning, like Long Short-Term Memory networks (LSTMs), have gained popularity due to their effectiveness in various natural language processing tasks. These tasks range from translating languages to summarizing content and recognizing speech. While LSTMs excel at handling small text corpora and have a strong handle on sentence-level syntactic structure, they typically struggle when it comes to modeling long-range dependencies and document-level structures, such as topics that span multiple sentences or entire documents.

To bridge this gap, a line of research has emerged where topic models – unsupervised algorithms that discover thematic patterns at the document level – are integrated with LMs to yield topic-guided language models (TGLMs). The premise is that such models should not only predict the next word in a sentence with a good understanding of syntax but also reflect global thematic structures characteristic of topic models.

However, a study examining the efficacy of these TGLMs calls this assumption into question. Upon comparing four TGLMs against standard LSTM-based LMs in a consistent experimental framework, the study reports that TGLMs fail to outperform the LMs, suggesting that the anticipated benefits of combining LMs with topic models may not materialize in practice. Furthermore, the topics extracted by TGLMs are generally no more coherent than those uncovered by standalone topic models, and in some cases, are qualitatively worse.

Another interesting facet of the study employs a technique known as probing. Probes are diagnostic tools used to determine how much specific information is encoded in the hidden layers of neural networks. Upon probing the LSTM models, the study reveals that the hidden states within these models already encode topic information – information that the incorporated topic models in TGLMs are supposed to imbue.

The authors point out that the lack of improvement in TGLMs over standard LMs is not just an issue with model architecture. Even when TGLMs condition on all prior words within a document, an approach that is supposed to provide a richer context for prediction, they do not outperform LMs. This raises questions about the extent to which neural language models inherently capture topic information without needing explicit topic modeling components.

Considering these findings, it becomes apparent that integrating LMs with topic models is not a guarantee of improved performance. The insights extend beyond LSTMs, suggesting that with more expressive models like transformers, explicitly incorporating topic models may still be unnecessary. This study thereby emphasizes the importance of rigorous evaluation and comparison to well-tuned baselines in the field of natural language processing. Furthermore, it advocates for transparency and reproducibility by making the code used for the study publicly available.

The study's insights underline the sophistication of neural language models in managing contextual and topical structures within text, indicating that future research may need to look beyond simply integrating different model types. Instead, the field must explore more novel ways to extract meaningful interpretable structures while leveraging the complex and inherently capable representations neural language models offer.