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Found 7 papers, 1 papers with code
NL-Augmenter: A Framework for Task-Sensitive Natural Language Augmentation
Data augmentation is an important component in the robustness evaluation of models in natural language processing (NLP) and in enhancing the diversity of the data they are trained on.
CANDLE: Decomposing Conditional and Conjunctive Queries for Task-Oriented Dialogue Systems
Domain-specific dialogue systems generally determine user intents by relying on sentence-level classifiers which mainly focus on single action sentences.
Automatic Construction of Evaluation Suites for Natural Language Generation Datasets
By applying this framework to the GEM generation benchmark, we propose an evaluation suite made of 80 challenge sets, demonstrate the kinds of analyses that it enables and shed light onto the limits of current generation models.
The GEM Benchmark: Natural Language Generation, its Evaluation and Metrics
We introduce GEM, a living benchmark for natural language Generation (NLG), its Evaluation, and Metrics.
Ranked #1 on
Data-to-Text Generation
on WebNLG ru
Abstractive Text Summarization
Cross-Lingual Abstractive Summarization
+5
Resolving Intent Ambiguities by Retrieving Discriminative Clarifying Questions
However, user queries appearing in natural language can be easily ambiguous and hence such a direct mapping might not be straightforward harming intent detection and eventually the overall performance of a dialogue system.
Benchmarking BioRelEx for Entity Tagging and Relation Extraction
Extracting relationships and interactions between different biological entities is still an extremely challenging problem but has not received much attention as much as extraction in other generic domains.
Syn-QG: Syntactic and Shallow Semantic Rules for Question Generation
Question Generation (QG) is fundamentally a simple syntactic transformation; however, many aspects of semantics influence what questions are good to form.
