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Found 12 papers, 3 papers with code
Feedback Attribution for Counterfactual Bandit Learning in Multi-Domain Spoken Language Understanding
With counterfactual bandit learning, models can be trained based on positive and negative feedback received for historical predictions, with no labeled data needed.
Multilingual Paraphrase Generation For Bootstrapping New Features in Task-Oriented Dialog Systems
As a solution, we propose a multilingual paraphrase generation model that can be used to generate novel utterances for a target feature and target language.
Recipes for Sequential Pre-training of Multilingual Encoder and Seq2Seq Models
(2) Conversely, using an encoder to warm-start seq2seq training, we show that by unfreezing the encoder partway through training, we can match task performance of a from-scratch seq2seq model.
Data-Efficient Paraphrase Generation to Bootstrap Intent Classification and Slot Labeling for New Features in Task-Oriented Dialog Systems
Recent progress through advanced neural models pushed the performance of task-oriented dialog systems to almost perfect accuracy on existing benchmark datasets for intent classification and slot labeling.
Leveraging User Paraphrasing Behavior In Dialog Systems To Automatically Collect Annotations For Long-Tail Utterances
In large-scale commercial dialog systems, users express the same request in a wide variety of alternative ways with a long tail of less frequent alternatives.
Ranking Generated Summaries by Correctness: An Interesting but Challenging Application for Natural Language Inference
While recent progress on abstractive summarization has led to remarkably fluent summaries, factual errors in generated summaries still severely limit their use in practice.
Abstractive Text Summarization
Natural Language Inference
+1
Fast Concept Mention Grouping for Concept Map-based Multi-Document Summarization
Concept map-based multi-document summarization has recently been proposed as a variant of the traditional summarization task with graph-structured summaries.
Concept-Map-Based Multi-Document Summarization using Concept Coreference Resolution and Global Importance Optimization
Concept-map-based multi-document summarization is a variant of traditional summarization that produces structured summaries in the form of concept maps.
GraphDocExplore: A Framework for the Experimental Comparison of Graph-based Document Exploration Techniques
Many techniques to automatically extract different types of graphs, showing for example entities or concepts and different relationships between them, have been suggested.
Bringing Structure into Summaries: Crowdsourcing a Benchmark Corpus of Concept Maps
Concept maps can be used to concisely represent important information and bring structure into large document collections.
