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Register a new userTransductive Learning for Unsupervised Text Style Transfer
التعلم المتنقل لنقل نمط النص غير المنشأ
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تستند نماذج نقل النمط غير المزروعة بشكل رئيسي إلى نهج التعلم الاستقرائي، والذي يمثل النمط كمعلمات أو معلمات فك الترميز، أو معلمات تمييزية، وتطبق مباشرة هذه القواعد العامة لحالات الاختبار. ومع ذلك، فإن عدم وجود Corpus الموازي يعيق قدرة طرق التعلم الاستقرائي هذه في هذه المهمة. نتيجة لذلك، من المحتمل أن تسبب التعبيرات النمطية غير المتناسقة الشديدة، مثل السلطة غير مهذب ". لمعالجة هذه المشكلة، نقترح نهجا تعليميا عبر جديد في هذه الورقة، بناء على تمثيل نمط السياق على علم الاسترجاع. على وجه التحديد، يتم استخدام وحدة فك ترميز تشفير الاهتمام مع إطار المسترد. أنه ينطوي على الجمل ذات الصلة أعلى K في النمط المستهدف في عملية النقل. وبهذه الطريقة، يمكننا أن نتعلم تضمين أسلوب مدرك السياق لتخفيف مشكلة عدم التناقض أعلاه. في هذه الورقة، يتم استخدام كل من وظائف استرجاع شديد (BM25) ووظائف استرجاع كثيفة (MIPS)، وتم تصميم وظيفتان موضوعيتان لتسهيل التعلم المشترك. تظهر النتائج التجريبية أن أسلوبنا تتفوق على العديد من خطوط الأساس القوية. نهج التعلم المتنقل المقترح عام وفعال لمهمة نقل النمط غير المنسق، وسوف نطبقه على الطريقة الوظيفتين الأخرى في المستقبل.
Unsupervised style transfer models are mainly based on an inductive learning approach, which represents the style as embeddings, decoder parameters, or discriminator parameters and directly applies these general rules to the test cases. However, the lacking of parallel corpus hinders the ability of these inductive learning methods on this task. As a result, it is likely to cause severe inconsistent style expressions, like the salad is rude'. To tackle this problem, we propose a novel transductive learning approach in this paper, based on a retrieval-based context-aware style representation. Specifically, an attentional encoder-decoder with a retriever framework is utilized. It involves top-K relevant sentences in the target style in the transfer process. In this way, we can learn a context-aware style embedding to alleviate the above inconsistency problem. In this paper, both sparse (BM25) and dense retrieval functions (MIPS) are used, and two objective functions are designed to facilitate joint learning. Experimental results show that our method outperforms several strong baselines. The proposed transductive learning approach is general and effective to the task of unsupervised style transfer, and we will apply it to the other two typical methods in the future.
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Text style transfer aims to controllably generate text with targeted stylistic changes while maintaining core meaning from the source sentence constant. Many of the existing style transfer benchmarks primarily focus on individual high-level semantic
changes (e.g. positive to negative), which enable controllability at a high level but do not offer fine-grained control involving sentence structure, emphasis, and content of the sentence. In this paper, we introduce a large-scale benchmark, StylePTB, with (1) paired sentences undergoing 21 fine-grained stylistic changes spanning atomic lexical, syntactic, semantic, and thematic transfers of text, as well as (2) compositions of multiple transfers which allow modeling of fine-grained stylistic changes as building blocks for more complex, high-level transfers. By benchmarking existing methods on StylePTB, we find that they struggle to model fine-grained changes and have an even more difficult time composing multiple styles. As a result, StylePTB brings novel challenges that we hope will encourage future research in controllable text style transfer, compositional models, and learning disentangled representations. Solving these challenges would present important steps towards controllable text generation.
The style transfer task (here style is used in a broad authorial'' sense with many aspects including register, sentence structure, and vocabulary choice) takes text input and rewrites it in a specified target style preserving the meaning, but alterin
g the style of the source text to match that of the target. Much of the existing research on this task depends on the use of parallel datasets. In this work we employ recent results in unsupervised cross-lingual language modeling (XLM) and machine translation to effect style transfer while treating the input data as unaligned. First, we show that adding content embeddings'' to the XLM which capture human-specified groupings of subject matter can improve performance over the baseline model. Evaluation of style transfer has often relied on metrics designed for machine translation which have received criticism of their suitability for this task. As a second contribution, we propose the use of a suite of classical stylometrics as a useful complement for evaluation. We select a few such measures and include these in the analysis of our results.
In most cases, the lack of parallel corpora makes it impossible to directly train supervised models for the text style transfer task. In this paper, we explore training algorithms that instead optimize reward functions that explicitly consider differ
ent aspects of the style-transferred outputs. In particular, we leverage semantic similarity metrics originally used for fine-tuning neural machine translation models to explicitly assess the preservation of content between system outputs and input texts. We also investigate the potential weaknesses of the existing automatic metrics and propose efficient strategies of using these metrics for training. The experimental results show that our model provides significant gains in both automatic and human evaluation over strong baselines, indicating the effectiveness of our proposed methods and training strategies.
Learning a good latent representation is essential for text style transfer, which generates a new sentence by changing the attributes of a given sentence while preserving its content. Most previous works adopt disentangled latent representation learn
ing to realize style transfer. We propose a novel text style transfer algorithm with entangled latent representation, and introduce a style classifier that can regulate the latent structure and transfer style. Moreover, our algorithm for style transfer applies to both single-attribute and multi-attribute transfer. Extensive experimental results show that our method generally outperforms state-of-the-art approaches.
Text style transfer involves rewriting the content of a source sentence in a target style. Despite there being a number of style tasks with available data, there has been limited systematic discussion of how text style datasets relate to each other.
This understanding, however, is likely to have implications for selecting multiple data sources for model training. While it is prudent to consider inherent stylistic properties when determining these relationships, we also must consider how a style is realized in a particular dataset. In this paper, we conduct several empirical analyses of existing text style datasets. Based on our results, we propose a categorization of stylistic and dataset properties to consider when utilizing or comparing text style datasets.
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