Quality Controlled Paraphrase Generation (2022)

Abstract: Paraphrase generation has been widely used in various downstream tasks. Most tasks benefit mainly from high quality paraphrases, namely those that are semantically similar to, yet linguistically diverse from, the original sentence. Generating high-quality paraphrases is challenging as it becomes increasingly hard to preserve meaning as linguistic diversity increases. Recent works achieve nice results by controlling specific aspects of the paraphrase, such as its syntactic tree. However, they do not allow to directly control the quality of the generated paraphrase, and suffer from low flexibility and scalability. Here we propose QCPG, a quality-guided controlled paraphrase generation model, that allows directly controlling the quality dimensions. Furthermore, we suggest a method that given a sentence, identifies points in the quality control space that are expected to yield optimal generated paraphrases. We show that our method is able to generate paraphrases which maintain the original meaning while achieving higher diversity than the uncontrolled baseline. The models, the code, and the data can be found in https://github.com/IBM/quality-controlled-paraphrase-generation .

## introduction paraphrase generation, namely rewriting a sentence using different words and/or syntax while preserving its meaning @xcite , is an important technique in natural language processing, that has been widely used in various downstream tasks including question answering @xcite , summarization @xcite , data augmentation @xcite and adversarial learning @xcite . however, not all paraphrases are equally useful. for most real-world applications, paraphrases which are too similar to the original sentence are of limited value, while those with high linguistic diversity, high quality region i.e. with large syntactic/lexical differences between the paraphrase and the original sentence, are more beneficial to the robustness and accuracy of automatic text evaluation and classification, and can avoid the blandness caused by repetitive patterns @xcite . the quality of paraphrases is often evaluated using three dimensions, where high quality paraphrases are those with high semantic similarity as well as high lexical and/or syntactic diversity @xcite . generating high quality paraphrases can be challenging (for both humans and automatic models) since it is increasingly difficult to preserve meaning with increasing linguistic diversity. indeed, when examining the quality of paraphrases among paraphrase generation datasets, one can find a wide range of paraphrase qualities, where the area of high quality is often very sparse (see figure 1 ). this in turn results in scarcity of supervised data for high-quality paraphrase generation. a recent approach aiming to produce high quality paraphrases is controlled paraphrase generation, which exposes control mechanisms that can be manipulated to produce diversity. while the controlled generation approaches have yielded impressive results, they require providing the model with very specific information regarding the target sentence, such as its parse tree @xcite or the list of keywords it needs to contain @xcite . however, for most downstream applications, the important property of the paraphrase is its overall quality, rather than its specific syntactic or lexical form. the over-specificity of existing controlbased methods not only complicates their usage and limits their scalability, but also hinders their coverage. thus, it would be desirable to develop a paraphrase generation model, which uses a simple mechanism for directly controlling paraphrase quality, while avoiding unnecessary complications associated with fine-grained controls. in this paper we propose qcpg, a quality controlled paraphrase generation model, that given an input sentence and quality constraints, represented by a three dimensional vector of semantic similarity, and syntactic and lexical distances, produces a target sentence that conforms to the quality constraints. our constraints are much simpler than previously suggested ones, such as parse trees or keyword lists, and leave the model the freedom to choose how to attain the desired quality levels. enabling the direct control of the three quality dimensions, allows flexibility with respect to the specific requirements of the task at hand, and opens a range of generation possibilities: paraphrases of various flavors (e.g. syntactically vs. lexically diverse), quasi-paraphrases (with lower semantic similarity), and even non-paraphrases which may be useful for downstream tasks (e.g. hard negative examples of sentences that are linguistically similar but have different meanings @xcite ). our results show that the qcpg model indeed enables controlling paraphrase quality along the three quality dimensions. furthermore, even though the training data is of mixed quality, and exhibits scarcity in the high quality area (see figure 1 ), our model is able to learn high quality paraphrasing behavior, i.e. it increases the linguistic diversity of the generated paraphrases without decreasing the semantic simi-larity compared to the uncontrolled baseline. ## method in this section we provide a general description of our approach. we first explain how the different quality dimensions are measured. we then describe the controlled paraphrase generation model, qcpg, and finally we suggest a method that given the task requirements, detects the input control values which maximize the quality of the generated paraphrases. figure 2 summarizes our proposed solution for generating controlled paraphrases, which is detailed in the rest of the section. ## related work many recent works on paraphrase generation have been focused on attempting to achieve high-quality paraphrases. these works can be divided into supervised and unsupervised approaches. supervised approaches to achieve diversity, some works focused on diverse decoding using heuristics such as hamming distance or distinct n-grams to preserve diverse options during beam search @xcite . other works generate multiple outputs by perturbing latent representations @xcite . or by using distinct generators @xcite . these methods achieve some diversity, but do not control generation in an interpretable manner. the works that are most similar to ours strive to gain diversity using controlled-paraphrase generation, by exposing control mechanisms that are manipulated to produce either lexically @xcite or syntactically @xcite diverse paraphrases. one approach is to use an exemplar sentence for guiding the syntax of the generated paraphrase @xcite @xcite ). an alternative is to directly employ constituency tree as the syntax guidance @xcite . @xcite promote syntactic diversity by conditioning over possible syntactic rearrangements of the input. @xcite use keywords as lexical guidance for the generation process. here we introduce a simple model for jointly controlling the lexical, syntactic and semantic aspects of the generated paraphrases. niu et al. (2020) rely on neural models to generate high quality paraphrases, using a decoding method that enforces diversity by preventing repetitive copying of the input tokens. @xcite optimize a quality oriented objective by casting paraphrase generation as an optimization problem, and searching the sentence space to find the optimal point. @xcite and siddique et al. (2020) use reinforcement learning with quality-oriented reward combining textual entailment, semantic similarity, expression diversity and fluency. in this work, we employ similar metrics for guiding the generation of paraphrases within the supervised framework. ## discussion in this paper, we propose a novel controlled paraphrase generation model, that leverages measures of paraphrase quality for encouraging the generation of paraphrases with desired quality. we demonstrate the high level of control achieved by the model, and suggest a method for coping with the challenging problem of finding suitable control values. aside from offering a simple and effective way for controlling models' output quality, the quality control paradigm enables a holistic view of the data, the training process and the final model analysis. namely: (i) examination of the training data through the lens of data quality enables to characterize the data at hand, its strengths and limitations. (ii) a quality-aware training process can be viewed as multi-task learning, where each quality level is a separate task with its own accurate supervision, as opposed to the standard quality-agnostic approach, where low quality data is in fact used as a poor supervision for a model which aims at generating higher quality output. (iii) analyzing the model behavior under different quality controls, allows finer understanding of the different model behaviors and the trade-offs between their output qualities. better understanding the expected output quality of neural nlg models, for different input quality controls, can increase the trust in their output. finally, our model analysis consistently shows that although the models generally follow the quality requirements, there is still room for improvement. a possible direction for future research is exploring methods, such as reinforcement learning, for further improving the ability of the model to satisfy the quality requirements.