The popularity of multimodal dialogue has stimulated the need for a new generation of dialogue agents with multimodal interactivity.
When users communicate with customer service, they may express their requirements by means of text, images, or even videos. 
Visual information usually acts as discriminators for product models, or indicators of product failures, which play an important role in the E-commerce scenario.
On the other hand, detailed information provided by the images is limited, and typically, customer service systems cannot understand the intent of users without the input text.
Thus, bridging the gap between the image and text is crucial for communicating with customers.
In this paper, we construct JDDC 2.1, a large-scale multimodal multi-turn dialogue dataset collected from a mainstream Chinese E-commerce platform, containing about 246K dialogue sessions, 3M utterances, and 507K images, along with product knowledge bases and image category annotations. 
Over our dataset, we jointly define four tasks: 
the multimodal dialogue response generation task,
the multimodal query rewriting task, the multimodal dialogue discourse parsing task, and the multimodal dialogue summarization task.
JDDC 2.1 is the first corpus with annotations for all the above tasks over the same dialogue sessions, which facilitates the comprehensive research around the dialogue.
In addition, we present several text-only and multimodal baselines and show the importance of visual information for these tasks. Our dataset and implements will be publicly available.

JDDC 2.1: A Multimodal Chinese Dialogue Dataset with Joint Tasks of Query Rewriting, Response Generation, Discourse Parsing, and Summarization

Pre-trained Language Models (PLMs) have shown effectiveness in various Natural Language Processing (NLP) tasks. Denoising autoencoder is one of the most successful pre-training frameworks, learning to recompose the original text given a noise-corrupted one. The existing studies mainly focus on injecting noises into the input. This paper introduces a simple yet effective pre-training paradigm, equipped with a knowledge-enhanced decoder that predicts the next entity token with noises in the prefix, explicitly strengthening the representation learning of entities that span over multiple input tokens. Specifically, when predicting the next token within an entity, we feed masks into the prefix in place of some of the previous ground-truth tokens that constitute the entity. Our model achieves new state-of-the-art results on two knowledge-driven data-to-text generation tasks with up to 2% BLEU gains.

PRINCE: Prefix-Masked Decoding for Knowledge Enhanced Sequence-to-Sequence Pre-Training

This paper studies the problem of generating a summary for a given sentence-image pair. Existing multimodal sequence-to-sequence approaches mainly focus on enhancing the decoder by visual signals, while ignoring that the image can improve the ability of the encoder to identify highlights of a news event or a document. Thus, we propose a multimodal selective gate network that considers reciprocal relationships between textual and multi-level visual features, including global image descriptor, activation grids, and object proposals, to select highlights of the event when encoding the source sentence. In addition, we introduce a modality regularization to encourage the summary to capture the highlights embedded in the image more accurately. To verify the generalization of our model, we adopt the multimodal selective gate to the text-based decoder and multimodal-based decoder. Experimental results on a public multimodal sentence summarization dataset demonstrate the advantage of our models over baselines. Further analysis suggests that our proposed multimodal selective gate network can effectively select important information in the input sentence.

Multimodal Sentence Summarization via Multimodal Selective Encoding

In this work, we present a model to generate e-commerce product summaries. The consistency between the generated summary and the product attributes is an essential criterion for the e- commerce product summarization task. To enhance the consistency, first, we encode the product attribute table to guide the process of summary generation. Second, we identify the attribute words from the vocabulary, and we constrain these attribute words can be presented in the summaries only through copying from the source, i.e., the attribute words not in the source cannot be generated. We construct a Chinese e-commerce product summarization dataset, and the experimental results on this dataset demonstrate that our models significantly improve the faithfulness.

On the Faithfulness for E-commerce Product Summarization

**Topics:** Theme Track 
Natural Language Generation 
Question Answering 
Resources and Evaluation

Virtual Poster Session 16

poster

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technical paper

POSTER21: Summarization

COLING, the International Conference on Computational Linguistics, is one of the premier conferences for natural language processing and computational linguistics. Often grouped within the field of artificial intelligence, but actually pre-dating the development of artificial intelligence, advances in computational linguistics and natural language processing are now some of the major drivers behind the use of artificial intelligence for commercial and social applications – for example, on-line search, machine translation and with voice-assisted conversational devices.

First established in 1965, the biennial COLING conference is held in diverse parts of the globe and attracts participants from both top-ranked research centers and emerging countries. Today, the most important developments in our field are taking place not only in universities and academic research institutes, but also in industrial research departments and in technological startups. COLING conferences provide opportunities for all these communities to showcase their exciting developments.

COLING 2020

COLING, the International Conference on Computational Linguistics, is one of the premier conferences for natural language processing and computational linguistics. Often grouped within the field of artificial intelligence, but actually pre-dating the development of artificial intelligence, advances in computational linguistics and natural language processing are now some of the major drivers behind the use of artificial intelligence for commercial and social applications – for example, on-line search, machine translation and with voice-assisted conversational devices.

Haoran Li

4

1

SHORT BIO

Presentations

JDDC 2.1: A Multimodal Chinese Dialogue Dataset with Joint Tasks of Query Rewriting, Response Generation, Discourse Parsing, and Summarization

PRINCE: Prefix-Masked Decoding for Knowledge Enhanced Sequence-to-Sequence Pre-Training

Multimodal Sentence Summarization via Multimodal Selective Encoding

On the Faithfulness for E-commerce Product Summarization

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