In this paper, we propose Sequence Span Rewriting (SSR), a self-supervised task for sequence-to-sequence (Seq2Seq) pre-training. SSR learns to refine the machine-generated imperfect text spans into ground truth text. SSR provides more fine-grained and informa- tive supervision in addition to the original text- infilling objective. Compared to the preva- lent text infilling objectives for Seq2Seq pre- training, SSR is naturally more consistent with many downstream generation tasks that re- quire sentence rewriting (e.g., text summariza- tion, question generation, grammatical error correction, and paraphrase generation). We conduct extensive experiments by using SSR to improve the typical Seq2Seq pre-trained model T5 in a continual pre-training setting and show substantial improvements over T5 on various natural language generation tasks.

Improving Sequence-to-Sequence Pre-training via Sequence Span Rewriting

In this paper, we propose Inverse Adversarial Training (IAT) algorithm for training neural dialogue systems to avoid generic responses and model dialogue history better. In contrast to standard adversarial training algorithms, IAT encourages the model to be sensitive to the perturbation in the dialogue history and therefore learning from perturbations. By giving higher rewards for responses whose output probability reduces more significantly when dialogue history is perturbed, the model is encouraged to generate more diverse and consistent responses. By penalizing the model when generating the same response given perturbed dialogue history, the model is forced to better capture dialogue history and generate more informative responses. Experimental results on two benchmark datasets show that our approach can better model dialogue history and generate more diverse and consistent responses. In addition, we point out a problem of the widely used maximum mutual information (MMI) based methods for improving the diversity of dialogue response generation models and demonstrate it empirically.

Learning from Perturbations: Diverse and Informative Dialogue Generation with Inverse Adversarial Training

1G - In Person poster Session

poster

EMNLP 2021 is planned to be a hybrid event in Punta Cana, Dominican Republic, with both on-site and fully virtual participation possible. The experience for on-site participants would closely approximate a normal pre-COVID *ACL conference, with 5-6 thematically organized parallel sessions and live Q/A and interactive discussion immediately after the talks. Presentations by virtual participants will be equitably interleaved with those of on-site participants, projected on the auditorium screens as if on-site, and also followed immediately by live Q/A and interactive discussion at a time during reasonable waking hours for the virtual presenter. For all participants, on-site and virtual, who are unable to attend a session due to either time-zone issues or because they are participating in another session live, talk recordings and slides will be available online at a minimum after the live presentation (and in many cases before as well), and questions may be submitted in advance on session-specific discussion boards and answered live in session with the usual visual aids if desired.

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EMNLP 2021

EMNLP 2021 is planned to be a hybrid event in Punta Cana, Dominican Republic, with both on-site and fully virtual participation possible.

Virtual Poster Session I: Generation


<i>**Session Chair: ** Ioannis Konstas</i><br><br>
<i>**Session Volunteer: ** Richard Plant</i><br><br>



3B: Dialog and Interactive Systems #2

technical paper

**Welcome to ACL-IJCNLP 2021!**

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IJCNLP-AACL 2021

The great event is jointly organized by the Association for Computational Linguistics (ACL) and Asian Federation of Natural Language Processing (AFNLP).

Wangchunshu Zhou

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5

SHORT BIO

Presentations

Improving Sequence-to-Sequence Pre-training via Sequence Span Rewriting

Improving Sequence-to-Sequence Pre-training via Sequence Span Rewriting

Learning from Perturbations: Diverse and Informative Dialogue Generation with Inverse Adversarial Training

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