Dynamic Word Embeddings (1702.08359v2)

Abstract: We present a probabilistic LLM for time-stamped text data which tracks the semantic evolution of individual words over time. The model represents words and contexts by latent trajectories in an embedding space. At each moment in time, the embedding vectors are inferred from a probabilistic version of word2vec [Mikolov et al., 2013]. These embedding vectors are connected in time through a latent diffusion process. We describe two scalable variational inference algorithms--skip-gram smoothing and skip-gram filtering--that allow us to train the model jointly over all times; thus learning on all data while simultaneously allowing word and context vectors to drift. Experimental results on three different corpora demonstrate that our dynamic model infers word embedding trajectories that are more interpretable and lead to higher predictive likelihoods than competing methods that are based on static models trained separately on time slices.

• The paper introduces a dynamic probabilistic model that integrates skip-gram filtering and smoothing to capture continuous semantic shifts.
• It employs scalable variational inference techniques to ensure smooth transitions and reduce noise in word embedding trajectories.
• Empirical results on diverse corpora demonstrate that the model outperforms static embeddings in tracking meaningful semantic evolution.

An Expert Overview of Dynamic Word Embeddings

The paper "Dynamic Word Embeddings" by Robert Bamler and Stephan Mandt introduces a probabilistic framework for modeling changes in word semantics over time by employing dynamic word embeddings. This research responds to the limitations of static word embeddings by presenting a mechanism that facilitates the inference of word trajectory in an embedding space, thereby accurately capturing the temporal evolution of language.

Core Contributions

The authors propose a novel dynamic model for word embeddings that integrates a probabilistic word2vec model with a latent diffusion process. This approach addresses several challenges in traditional static embeddings:

1. Continuity and Smoothness: The authors develop two scalable variational inference algorithms, namely skip-gram smoothing and skip-gram filtering, which enable the joint training of word embeddings over time. This continuous framework mitigates issues inherent in partitioning corpora into discrete time bins, which often leads to inconsistent and less interpretable embeddings due to non-convexity and overfitting in smaller datasets.
2. Probabilistic State-Space Modeling: The dynamic model embodies a probabilistic state-space approach, utilizing skip-gram models connected via a latent trajectory over continuous time. This enhancement not only smooths out the noise in word embedding trajectories but also ensures robust temporal sharing of information across all data points, a significant improvement over the purely static models.
3. Empirical Evidence: Through extensive experimentation on three different corpora—from the historic Google Books corpus to State of the Union addresses and contemporary Twitter data—they demonstrate that their dynamic model achieves higher predictive likelihoods than static counterparts. This empirical evidence suggests that the model is more adept at uncovering meaningful semantic shifts, such as the semantic evolution of words due to technology or cultural changes.

Implications and Future Directions

The incorporation of time-stamped text data into LLMs represents a key advancement in understanding and leveraging language dynamics. The ability to automatically detect words with evolving meanings highlights potential applications in linguistics, historical analyses, and cultural studies. For instance, the algorithm could be used to analyze how public sentiment toward political figures shifts over time, or how technical terms evolve with scientific progress.

Theoretically, the model sets the stage for further exploration into non-stationary environments where varied dynamics of language evolution can be systematically studied. Moreover, utilizing structured variational inference with tridiagonal precision matrices opens doors to broad application in any domain where temporal variation and smooth transitions are crucial.

Conclusion

By proposing a robust and scalable dynamic model for word embeddings, this work represents a meaningful progression from static to dynamic representations capable of capturing semantic drift. Future investigations may expand on this model's capacity to handle broader language phenomena and adapt to real-time data contexts, potentially integrating with live social media feeds for instantaneous linguistic analysis. Hence, the development of dynamic embeddings not only influences natural language processing tasks but also enriches the understanding of language adaptation in the digital age.