Lexically Constrained Decoding for Sequence Generation Using Grid Beam Search (1704.07138v2)

Abstract: We present Grid Beam Search (GBS), an algorithm which extends beam search to allow the inclusion of pre-specified lexical constraints. The algorithm can be used with any model that generates a sequence $ \mathbf{\hat{y}} = {y_{0}\ldots y_{T}} $, by maximizing $ p(\mathbf{y} | \mathbf{x}) = \prod\limits_{t}p(y_{t} | \mathbf{x}; {y_{0} \ldots y_{t-1}}) $. Lexical constraints take the form of phrases or words that must be present in the output sequence. This is a very general way to incorporate additional knowledge into a model's output without requiring any modification of the model parameters or training data. We demonstrate the feasibility and flexibility of Lexically Constrained Decoding by conducting experiments on Neural Interactive-Predictive Translation, as well as Domain Adaptation for Neural Machine Translation. Experiments show that GBS can provide large improvements in translation quality in interactive scenarios, and that, even without any user input, GBS can be used to achieve significant gains in performance in domain adaptation scenarios.

• The paper proposes grid beam search, a method that integrates lexical constraints directly into sequence generation for controlled outputs.
• The approach organizes beams in a grid structure, allowing efficient handling of single-word and phrase constraints in tasks like translation and summarization.
• Experimental results demonstrate enhanced translation quality through interactive post-editing and effective domain adaptation using constraint-guided outputs.

Lexically Constrained Decoding for Sequence Generation Using Grid Beam Search

The paper presents an exploration of lexically constrained decoding for sequence generation tasks using an algorithm named Grid Beam Search (GBS). This work seeks to address the need for integrating lexical constraints, such as specific phrases or words, into sequence outputs generated by NLP models. This innovation is particularly important as it allows for the enhanced usability of sequence-based models by incorporating domain-specific knowledge or user-provided constraints without altering the core model architecture or training data.

Algorithmic Insights

GBS extends the traditional beam search method favored for its flexibility and effectiveness in sequence generation tasks, such as machine translation and text summarization. Unlike standard beam search which optimizes sequence generation purely based on probability estimates, GBS introduces a grid structure that enables the incorporation of lexical constraints. Each grid level corresponds to incorporating one token of a constraint sequence, allowing for precise control over the presence and position of constraints within the generated sequence.

1. Constraint Management: In GBS, constraints can be single words or multi-word phrases. The grid facilitates this by maintaining beams based on two indices: the timestep in the output sequence and the count of constraints covered. This novel organization allows for generating, starting, and continuing constraints in a flexible manner.
2. Efficiency and Implementation: Although the inherent complexity of GBS is increased due to the multiplicative factor introduced by constraints, the authors suggest parallelization since beams at the same timestep can be processed independently. Additionally, the implementation leverages subword tokenization to handle potentially unknown lexical items in constraints, thus enhancing model robustness.

Experimental Evaluation

The paper evaluates GBS within two major experimental settings: interactive machine translation (MT) and domain adaptation.

1. Interactive MT via Pick-Revise: Simulating interactive post-editing scenarios, GBS demonstrates significant improvement in translation quality through user-defined iterative constraint cycles. This rigorous testing validated that GBS can progressively enhance outputs by integrating user corrections during each cycle.
2. Domain Adaptation: By using domain-specific terminologies as constraints, GBS adapts general-domain MT models to specialized domains without necessitating retraining. The experiments underscore that even automatically generated terminologies can induce substantial translation quality enhancements.

Theoretical and Practical Implications

The primary theoretical contribution is the extension of beam search methodology to handle hard constraints in sequence generation, thereby bridging a gap between purely data-driven and interactive/user-guided methods in NLP. Practically, this offers an opportunity for deploying pre-trained models in domain-specific applications effectively, reducing the dependency on large domain-specific datasets and computational resources for retraining.

Speculation on Future Developments

Looking forward, the integration of GBS in other sequence generation applications, such as dialog systems or image captioning, presents an exciting avenue for exploration. The current work sets a strong foundation for future research to explore constraint-aware neural architectures that can directly learn to prioritize or incorporate constraints during training. Further advancements might also explore enhancing constraint flexibility using dynamic prioritization strategies to refine user interaction in real-time adaptive systems.

In conclusion, lexically constrained decoding using GBS represents a substantial methodological advancement for improving sequence generation models' adaptability and precision. It offers practical solutions for real-world applications by facilitating the integration of explicit knowledge constraints, thus broadening the applicability of pre-existing NLP models across diverse domains.