Shakespearizing Modern Language Using Copy-Enriched Sequence-to-Sequence Models (1707.01161v2)

Abstract: Variations in writing styles are commonly used to adapt the content to a specific context, audience, or purpose. However, applying stylistic variations is still by and large a manual process, and there have been little efforts towards automating it. In this paper we explore automated methods to transform text from modern English to Shakespearean English using an end to end trainable neural model with pointers to enable copy action. To tackle limited amount of parallel data, we pre-train embeddings of words by leveraging external dictionaries mapping Shakespearean words to modern English words as well as additional text. Our methods are able to get a BLEU score of 31+, an improvement of ~6 points above the strongest baseline. We publicly release our code to foster further research in this area.

Shakespearizing Modern Language Using Copy-Enriched Sequence-to-Sequence Models

The paper "Shakespearizing Modern Language Using Copy-Enriched Sequence-to-Sequence Models" presents a method for converting modern English text into a style that mimics the Elizabethan prose of William Shakespeare. This linguistic transformation is achieved through a novel application of sequence-to-sequence (seq2seq) models, augmented with a copy mechanism to preserve key token information and style elements from the source text to the target text.

The authors investigate the efficacy of this transformed seq2seq model through comparison with traditional statistical machine translation (SMT) techniques. The SMT approach typically involves the decomposition of the probability distribution $P(t|s)$, where $t$ and $s$ are the target and source sequences, respectively, using a noisy-channel formulation. Several models are developed for this purpose, differentiated by their training methodologies and how they leverage external resources like large corpora or parallel text data. The SMT models evaluated include Model 1 through Model 9, where models differ primarily by the n-gram LLM employed, the use of additional parallel pairs during training, and the incorporation of external corpora such as the Penn TreeBank.

The paper underlines an intriguing approach with the introduction of a copy mechanism within their neural architectures—referred to as Copy, SimpleS2S, and Copy+SL (Sentinel Loss). The results, as shown in their BLEU score evaluations, highlight the superior performance of augmented models compared to baseline models. Specifically, configurations where the copy mechanism was coupled with encoder-decoder embedding sharing and sentinel loss displayed notable increases in performance. For instance, the Copy+SL model with the RetroFixed variant achieved a BLEU score of 27.66, highlighting the benefit of these novel enhancements.

The table of results emphasizes the significant variation in model performance contingent on the architecture and training setup used. While conventional SMT models had respectable BLEU scores with Model 7 reaching 24.39, the copy-enhanced seq2seq models yielded better balance between transformation fidelity and creative adaptation with a BLEU score peaking at 31.12 in the Copy model with RetroExtFixed settings.

This research has dual theoretical and practical implications. Theoretically, it demonstrates the feasibility of incorporating a copy mechanism within a seq2seq framework to handle stylistic text translation tasks. Practically, this framework can be extended beyond literary transformations to encompass more complex stylistic and domain adaptation tasks without sacrificing content integrity. Speculating on future developments, this approach can enrich dialogue systems, style transfer applications, and educational tools by providing stylistically consistent outputs tailored to specific cultural or temporal linguistic norms.

The rigorous approach to evaluating both statistical and machine learning methodologies presents a compelling case for integrating various linguistic transfer mechanisms into modern NLP systems for enhanced cultural and stylistic articulation. This work, therefore, constitutes a meaningful contribution to the field of natural language processing and computational linguistics.