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Found 21 papers, 7 papers with code
MovieChats: Chat like Humans in a Closed Domain
In this work, we take a close look at the movie domain and present a large-scale high-quality corpus with fine-grained annotations in hope of pushing the limit of movie-domain chatbots.
Preventing Author Profiling through Zero-Shot Multilingual Back-Translation
Documents as short as a single sentence may inadvertently reveal sensitive information about their authors, including e. g. their gender or ethnicity.
The SelectGen Challenge: Finding the Best Training Samples for Few-Shot Neural Text Generation
We propose a shared task on training instance selection for few-shot neural text generation.
On Training Instance Selection for Few-Shot Neural Text Generation
In this work, we present a study on training instance selection in few-shot neural text generation.
Learning Fine-grained Fact-Article Correspondence in Legal Cases
We believe that learning fine-grained correspondence between each single fact and law articles is crucial for an accurate and trustworthy AI system.
Neural Data-to-Text Generation with LM-based Text Augmentation
Our approach automatically augments the data available for training by (i) generating new text samples based on replacing specific values by alternative ones from the same category, (ii) generating new text samples based on GPT-2, and (iii) proposing an automatic method for pairing the new text samples with data samples.
Data Augmentation for Multiclass Utterance Classification -- A Systematic Study
Utterance classification is a key component in many conversational systems.
Cross-Domain Learning for Classifying Propaganda in Online Contents
Prior work has focused on supervised learning with training data from the same domain.
DART: A Lightweight Quality-Suggestive Data-to-Text Annotation Tool
We present a lightweight annotation tool, the Data AnnotatoR Tool (DART), for the general task of labeling structured data with textual descriptions.
Integrating Image Captioning with Rule-based Entity Masking
Particularly, these image features are subdivided into global and local features, where global features are extracted from the global representation of the image, while local features are extracted from the objects detected locally in an image.
Diversifying Dialogue Generation with Non-Conversational Text
Neural network-based sequence-to-sequence (seq2seq) models strongly suffer from the low-diversity problem when it comes to open-domain dialogue generation.
Neural Data-to-Text Generation via Jointly Learning the Segmentation and Correspondence
The neural attention model has achieved great success in data-to-text generation tasks.
Unsupervised Pidgin Text Generation By Pivoting English Data and Self-Training
In this work, we develop techniques targeted at bridging the gap between Pidgin English and English in the context of natural language generation.
Improving Latent Alignment in Text Summarization by Generalizing the Pointer Generator
Pointer Generators have been the de facto standard for modern summarization systems.
Select and Attend: Towards Controllable Content Selection in Text Generation
As a result, the content to be described in the text cannot be explicitly controlled.
Unsupervised Rewriter for Multi-Sentence Compression
First, the word graph approach that simply concatenates fragments from multiple sentences may yield non-fluent or ungrammatical compression.
Improving Multi-turn Dialogue Modelling with Utterance ReWriter
To properly train the utterance rewriter, we collect a new dataset with human annotations and introduce a Transformer-based utterance rewriting architecture using the pointer network.
NEXUS Network: Connecting the Preceding and the Following in Dialogue Generation
Sequence-to-Sequence (seq2seq) models have become overwhelmingly popular in building end-to-end trainable dialogue systems.
Improving Variational Encoder-Decoders in Dialogue Generation
Variational encoder-decoders (VEDs) have shown promising results in dialogue generation.
DailyDialog: A Manually Labelled Multi-turn Dialogue Dataset
We develop a high-quality multi-turn dialog dataset, DailyDialog, which is intriguing in several aspects.
A Conditional Variational Framework for Dialog Generation
Deep latent variable models have been shown to facilitate the response generation for open-domain dialog systems.