Self-Training for Compositional Neural NLG in Task-Oriented Dialogue (2021)

Abstract: Neural approaches to natural language generation in task-oriented dialogue have typically required large amounts of annotated training data to achieve satisfactory performance, especially when generating from compositional inputs. To address this issue, we show that self-training enhanced with constrained decoding yields large gains in data efficiency on a conversational weather dataset that employs compositional meaning representations. In particular, our experiments indicate that self-training with constrained decoding can enable sequence-to-sequence models to achieve satisfactory quality using vanilla decoding with five to ten times less data than with ordinary supervised baseline; moreover, by leveraging pretrained models, data efficiency can be increased further to fifty times. We confirm the main automatic results with human evaluations and show that they extend to an enhanced, compositional version of the E2E dataset. The end result is an approach that makes it possible to achieve acceptable performance on compositional NLG tasks using hundreds rather than tens of thousands of training samples.

## introduction neural approaches to natural language generation (nlg) have received increasing attention due to their flexibility and end-to-end trainability @xcite @xcite . however, despite using simplistic input meaning representations (mr), most neural models require large quantities of clean annotated training data in order to obtain good performance. as such, the time and expense required to obtain sufficient training data is a significant obstacle to deploying neural nlg models at scale. to enable richer task-oriented dialogue, @xcite argue for using compositional, tree-structured mrs that include discourse rela-tions, emphasizing the need for applications to exert control over these relations when generating text. perhaps not surprisingly, their compositional input mrs further exacerbate annotated data needs. to address this issue, @xcite introduce a novel constrained decoding technique that nearly always yields correct output even in challenging cases. however, their constrained decoding method incurs a substantial runtime cost, making it too slow to deploy in task-oriented dialogue systems where low latency is a priority. thus, finding ways to improve data efficiency for training models that perform satisfactorily with vanilla decoding remains an important challenge. in order to reduce annotated data needs, @xcite and @xcite propose self-training methods for nlg, though they do not explore self-training for the more challenging case of generating from compositional input representations. @xcite do explore self-training with compositional inputs, but they do not consider constrained decoding. in this paper, we investigate for the first time whether constrained decoding can be used during self-training to enhance data efficiency for compositional neural nlg, since the speed of constrained decoding is much less of a concern during self-training than it is at runtime in dialogue systems. in particular, we adapt and extend @xcite approach to self-training for mt to the setting of neural nlg from compositional mrs, comparing vanilla self-training to self-training enhanced with constrained decoding as well as with reverse model reranking @xcite , a simpler technique where the n-best outputs of the forward model are reranked using scores from a reverse model. in both cases, the idea is to enhance the quality of the pseudo-annotated texts created during self-training, so that self-training can more successfully avoid entrenching the model's own 2019 ) conversational weather dataset. in the actual dataset, discourse relations have a ds prefix (e.g., ds contrast), dialog acts have a dg prefix (e.g, dg inform) and arguments have an arg prefix (e.g., arg city); these are elided here for brevity. our contributions are two-fold. on @xcite conversational weather dataset, we show that using constrained decoding during selftraining and their seq2seq-tree model at runtime yields comparable performance with 20% of the annotated data as using the full training set in supervised fashion, and by leveraging pretrained models, annotated data needs can be further reduced to 2%. we then confirm the main automatic metric results with human evaluations and show that they hold for @xcite enhanced version of the e2e dataset @xcite . ## method neural nlg seq2seq models aim to generate a natural language text @xmath0, • • • , y |y| from a meaning representation @xmath1, • • • , x |x| by modeling the conditional probability ## conclusion and future work in this paper, we have shown that using selftraining with constrained decoding in compositional neural nlg can deliver large gains in data efficiency, enabling seq2seq models to achieve satisfactory quality using vanilla decoding with much less annotated data. the idea of using constrained decoding with self-training rather than for runtime inference is a very simple one, but ours is the first paper to investigate the idea, and we show via thorough experimentation and evaluation that it works remarkably well. in our experiments, we found that lstm models trained from scratch can increase data efficiency by a factor of at least 5, while pretrained bart models yielded a 50 times increase, achieving essentially comparable levels of correctness and grammaticality using only 2% of the existing training data. as such, the approach promises to help pave the way towards developing systems with mere hundreds rather than tens of thousands of annotated samples, potentially eliminating the need for crowdsourcing in system development. in future work, it would be exploring ways of at least partially automatically adding semantic annotations to the target texts using methods that treat such annotations as latent @xcite to facilitate using our approach on a new task or dataset.