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Found 11 papers, 4 papers with code
A Hybrid Rule-Based and Neural Coreference Resolution System with an Evaluation on Dutch Literature
We introduce a modular, hybrid coreference resolution system that extends a rule-based baseline with three neural classifiers for the subtasks mention detection, mention attributes (gender, animacy, number), and pronoun resolution.
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.
Multi-perspective Alignment for Increasing Naturalness in Neural Machine Translation
Neural machine translation (NMT) systems amplify lexical biases present in their training data, leading to artificially impoverished language in output translations.
INCLUDE: Evaluating Multilingual Language Understanding with Regional Knowledge
The performance differential of large language models (LLM) between languages hinders their effective deployment in many regions, inhibiting the potential economic and societal value of generative AI tools in many communities.
How Good is Your Wikipedia?
Wikipedia's perceived high quality and broad language coverage have established it as a fundamental resource in multilingual NLP.
Towards Tailored Recovery of Lexical Diversity in Literary Machine Translation
Thus, rather than aiming for the rigid increase of lexical diversity, we reframe the task as recovering what is lost in the machine translation process.
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.
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.
Multilingual Gradient Word-Order Typology from Universal Dependencies
While information from the field of linguistic typology has the potential to improve performance on NLP tasks, reliable typological data is a prerequisite.
CreoleVal: Multilingual Multitask Benchmarks for Creoles
Creoles represent an under-explored and marginalized group of languages, with few available resources for NLP research. While the genealogical ties between Creoles and a number of highly-resourced languages imply a significant potential for transfer learning, this potential is hampered due to this lack of annotated data.
The Past, Present, and Future of Typological Databases in NLP
We propose that such a view of typology has significant potential in the future, including in language modeling in low-resource scenarios.
