Relation-Constrained Decoding for Text Generation

The dominant paradigm for neural text generation nowadays is seq2seq learning with large-scale pretrained language models. However, it is usually difficult to manually constrain the generation process of these models. Prior studies have introduced Lexically Constrained Decoding (LCD) to ensure the presence of prespecified words or phrases in the output. However, simply applying lexical constraints has no guarantee of the grammatical or semantic relations between words. Thus, more elaborate constraints are needed. To this end, we first propose a new constrained decoding scenario named Relation-Constrained Decoding (RCD), which requires the model's output to contain several given word pairs with respect to the given relations between them. For this scenario, we present a novel plug-andplay decoding algorithm named RElation-guided probability Surgery and bEam ALlocation (RESEAL), which can handle different categories of relations, e.g., syntactical relations or factual relations. Moreover, RESEAL can adaptively "reseal" the relations to form a high-quality sentence, which can be applied to the inference stage of any autoregressive text generation model. To evaluate our method, we first construct an RCD benchmark based on dependency relations from treebanks with annotated dependencies. Experimental results demonstrate that our approach can achieve better preservation of the input dependency relations compared to previous methods. To further illustrate the effectiveness of RESEAL, we apply our method to three downstream tasks: sentence summarization, fact-based text editing, and data-to-text generation. We observe an improvement in generation quality. The source code is available at https://github.com/CasparSwift/RESEAL . * Equal contribution. 36th Conference on Neural Information Processing Systems (NeurIPS 2022).