Syntax-Guided Controlled Generation of Paraphrases

By Ashutosh Kumar, Kabir Ahuja, Raghuram Vadapalli, Partha P. Talukdar. 2020

105 citations recorded by the source corpus.

Abstract Given a sentence (e.g., “I like mangoes”) and a constraint (e.g., sentiment flip), the goal of controlled text generation is to produce a sentence that adapts the input sentence to meet the requirements of the constraint (e.g., “I hate mangoes”). Going beyond such simple constraints, recent work has started exploring the incorporation of complex syntactic-guidance as constraints in the task of controlled paraphrase generation. In these methods, syntactic-guidance is sourced from a separate exemplar sentence. However, these prior works have only utilized limited syntactic information available in the parse tree of the exemplar sentence. We address this limitation in the paper and propose Syntax Guided Controlled Paraphraser (SGCP), an end-to-end framework for syntactic paraphrase generation. We find that Sgcp can generate syntax-conforming sentences while not compromising on relevance. We perform extensive automated and human evaluations over multiple real-world English language datasets to demonstrate the efficacy of Sgcp over state-of-the-art baselines. To drive future research, we have made Sgcp’s source code available.1

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Natural Language Processing

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Citations in the corpus

Tailor: Generating and Perturbing Text with Semantic Controls (2021, background): “Recent works incorporating syntactic structures for paraphrasing (Iyyer et al., 2018; Chen et al., 2019; Bao et al., 2019; Kumar et al., 2020; Sun et al., 2021; Huang and Chang, 2021) or discrete semantic signatures for diverse generation (Weir et al., 2020) are similar to T ailor in their…”
A Survey of Data Augmentation Approaches for NLP (2021, background): “Other approaches include syntactic or controlled paraphrasing (Iyyer et al., 2018; Kumar et al., 2020), document-level paraphrasing (Gangal et al., 2021), augmenting misclassiﬁed examples (Dreossi et al., 2018), BERT cross-encoder labeling of new inputs (Thakur et al., 2021), guided generation…”
Impossible Distillation for Paraphrasing and Summarization: How to Make High-quality Lemonade out of Small, Low-quality Model (2023, methodology): “We use three human-curated benchmarks spanning general and domain-specific paraphrase generation: MRPC (Dolan and Brockett, 2005), ParaNMT-small (Kumar et al., 2020), and ParaSCI-arXiv (Dong et al., 2021).”
AESOP: Paraphrase Generation with Adaptive Syntactic Control (2021, methodology): “After qualitative checks, we share the same ﬁnding with previous works (Kumar et al., 2020; Chen et al., 2019a) that SCPN tends to strictly adhere to syntactic parses at the cost of semantics.”
Paraphrase Generation: A Survey of the State of the Art (2021, background): “Some works control diversity by provid-Quora (Kumar et al., 2020) 66.9 45.0 - - - - - - Table 3: State-of-the-art performance on Quora dataset and MSCOCO dataset. ing control signals to the decoder.”
Generating Syntactically Controlled Paraphrases without Using Annotated Parallel Pairs (2021, methodology): “…(Logeswaran et al., 2018), plots (Ammanabrolu et al., 2020; Fan et al., 2019; Tambwekar et al., 2019; Yao et al., 2019; Goldfarb-Tarrant et al., 2019, 2020), societal bias (Wallace et al., 2019; Sheng et al., 2020b,a), and syntax (Iyyer et al., 2018; Goyal and Durrett, 2020; Kumar et al., 2020).”