Generating High-Quality and Informative Conversation Responses with Sequence-to-Sequence Models

Shao, Louis; Gouws, Stephan; Britz, Denny; Goldie, Anna; Strope, Brian; Kurzweil, Ray

doi:10.48550/arxiv.1701.03185

Cited by 23 publications

(20 citation statements)

References 3 publications

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“…And whether a model can generate diverse (Xu et al, 2018;Baheti et al, 2018), coherent (Li et al, 2016bTian et al, 2017;Bosselut et al, 2018;Adiwardana et al, 2020), informative (Shao et al, 2017;Lewis et al, 2017;Ghazvininejad et al, 2017;Young et al, 2017;Zhao et al, 2019) and knowledge-fused (Hua et al, 2020;Zhao et al, 2020;He et al, 2020) responses or not has become metrics to evaluate a dialog generation model. However, the mainly researches described above are developed on textual only and the development of multimodal dialog generation is relatively slow since the lack of large-scale datasets.…”

Section: Dialog Generationmentioning

confidence: 99%

OpenViDial 2.0: A Larger-Scale, Open-Domain Dialogue Generation Dataset with Visual Contexts

Wang¹,

Meng²,

Li³

et al. 2021

Preprint

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In order to better simulate the real human conversation process, models need to generate dialogue utterances based on not only preceding textual contexts but also visual contexts. However, with the development of multi-modal dialogue learning, the dataset scale gradually becomes a bottleneck. In this report, we release OpenViDial 2.0, a larger-scale open-domain multi-modal dialogue dataset compared to the previous version OpenViDial 1.0 (Meng et al., 2020). OpenViDial 2.0 contains a total number of 5.6 million dialogue turns extracted from either movies or TV series from different resources, and each dialogue turn is paired with its corresponding visual context. We hope this large-scale dataset can help facilitate future researches on open-domain multi-modal dialog generation, e.g., multi-modal pretraining for dialogue generation. 1

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Section: Dialog Generationmentioning

confidence: 99%

OpenViDial 2.0: A Larger-Scale, Open-Domain Dialogue Generation Dataset with Visual Contexts

Wang¹,

Meng²,

Li³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Another problem of current commenting systems arises from the limitation of the Seq2Seq framework (Sutskever et al, 2014), which has been known to suffer from generating dull and responses that are irrelevant to the input articles (Li et al, 2015;Wei et al, 2019;Shao et al, 2017). As shown in Figure 1, the Seq2Seq baseline generates I love this movie for the input article, despite the fact that Ode of joy is not a movie, but a TV series.…”

Section: Angermentioning

confidence: 99%

Towards Controlled and Diverse Generation of Article Comments

Zhang,

Wang

2021

Preprint

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Much research in recent years has focused on automatic article commenting. However, few of previous studies focus on the controllable generation of comments. Besides, they tend to generate dull and commonplace comments, which further limits their practical application. In this paper, we make the first step towards controllable generation of comments, by building a system that can explicitly control the emotion of the generated comments. To achieve this, we associate each kind of emotion category with an embedding and adopt a dynamic fusion mechanism to fuse this embedding into the decoder. A sentence-level emotion classifier is further employed to better guide the model to generate comments expressing the desired emotion. To increase the diversity of the generated comments, we propose a hierarchical copy mechanism that allows our model to directly copy words from the input articles. We also propose a restricted beam search (RBS) algorithm to increase intrasentence diversity. Experimental results show that our model can generate informative and diverse comments that express the desired emotions with high accuracy.

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“…Once the barycenter p was computed, the result was fed into a beam search (beam size B = 5), whose output, in turn, was then given to the captioner's LSTM and the process continued until a stop symbol (EOS) was generated. In order to exploit the controllable entropy of W. barycenter via the entropic regualrization parameter ε, we also decode using randomized Beam search of (Shao et al, 2017), where instead of maintaining the top k values, we sample D candidates in each beam. The smoothness of the barycenter in semantic clusters and its controllable entropy promotes diversity in the resulting captions.…”

Section: Image Captioningmentioning

confidence: 99%

Wasserstein Barycenter Model Ensembling

Dognin¹,

Melnyk²,

Mroueh³

et al. 2019

Preprint

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In this paper we propose to perform model ensembling in a multiclass or a multilabel learning setting using Wasserstein (W.) barycenters. Optimal transport metrics, such as the Wasserstein distance, allow incorporating semantic side information such as word embeddings. Using W. barycenters to find the consensus between models allows us to balance confidence and semantics in finding the agreement between the models. We show applications of Wasserstein ensembling in attribute-based classification, multilabel learning and image captioning generation. These results show that the W. ensembling is a viable alternative to the basic geometric or arithmetic mean ensembling.

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Generating High-Quality and Informative Conversation Responses with Sequence-to-Sequence Models

Cited by 23 publications

References 3 publications

OpenViDial 2.0: A Larger-Scale, Open-Domain Dialogue Generation Dataset with Visual Contexts

OpenViDial 2.0: A Larger-Scale, Open-Domain Dialogue Generation Dataset with Visual Contexts

Towards Controlled and Diverse Generation of Article Comments

Wasserstein Barycenter Model Ensembling

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