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Found 8 papers, 3 papers with code
RUG-1-Pegasussers at SemEval-2022 Task 3: Data Generation Methods to Improve Recognizing Appropriate Taxonomic Word Relations
This paper describes our system created for the SemEval 2022 Task 3: Presupposed Taxonomies - Evaluating Neural-network Semantics.
Transparent Semantic Parsing with Universal Dependencies Using Graph Transformations
Even though many recent semantic parsers are based on deep learning methods, we should not forget that rule-based alternatives might offer advantages over neural approaches with respect to transparency, portability, and explainability.
The Roles of English in Evaluating Multilingual Language Models
In this position paper, we lay out two roles of English in multilingual LM evaluations: as an interface and as a natural language.
How Good is Your Wikipedia?
Wikipedia's perceived high quality and broad language coverage have established it as a fundamental resource in multilingual NLP.
A Principled Framework for Evaluating on Typologically Diverse Languages
We compare sampling methods with a range of metrics and find that our systematic methods consistently retrieve more typologically diverse language selections than previous methods in NLP.
Engineering Conversational Search Systems: A Review of Applications, Architectures, and Functional Components
Conversational search systems enable information retrieval via natural language interactions, with the goal of maximizing users' information gain over multiple dialogue turns.
What is "Typological Diversity" in NLP?
We recommend future work to include an operationalization of 'typological diversity' that empirically justifies the diversity of language samples.
Detecting ChatGPT: A Survey of the State of Detecting ChatGPT-Generated Text
While recent advancements in the capabilities and widespread accessibility of generative language models, such as ChatGPT (OpenAI, 2022), have brought about various benefits by generating fluent human-like text, the task of distinguishing between human- and large language model (LLM) generated text has emerged as a crucial problem.
