chore: update paper trend data

Author: github-actions[bot]

data/full/emnlp-2023.json

@@ -55801,7 +55801,7 @@
       "Marek Kubis",
       "Paweł Skórzewski",
       "Marcin Sowański",
-      "Tomasz Zietkiewicz"
+      "Tomasz Ziętkiewicz"
     ],
     "abstract": "In a spoken dialogue system, an NLU model is preceded by a speech recognition system that can deteriorate the performance of natural language understanding. This paper proposes a method for investigating the impact of speech recognition errors on the performance of natural language understanding models. The proposed method combines the back transcription procedure with a fine-grained technique for categorizing the errors that affect the performance of NLU models. The method relies on the usage of synthesized speech for NLU evaluation. We show that the use of synthesized speech in place of audio recording does not change the outcomes of the presented technique in a significant way.",
     "year": 2023,

data/full/emnlp-2025.json

@@ -47154,7 +47154,7 @@
       "Yifei Song",
       "William Soto Martinez",
       "Anna Nikiforovskaya",
-      "Evan Parker Kelly Chapple",
+      "Evan Chapple",
       "Claire Gardent"
     ],
     "abstract": "Most work on Knowledge Graph (KG) verbalisation is monolingual leaving open the question of how to scale KG-to-Text generation to languages with varying amounts of resources. In this work, we explore KG-to-Text generation on nine languages including five high-resource (HR) languages (English, Chinese, French, Spanish, Russian) and four low-resource (LR) languages (Breton, Irish, Maltese, Welsh). We first construct silver multilingual training data for all nine languages and new gold out-of-domain test data for the five HR languages. Using this data and already available in-domain test sets for 7 of our 9 languages, we then compare three strategies: (1) NLG+MT—a state-of-the-art KG-to-English model followed by Machine Translation (MT) into the target language; (2) FTMT—multilingual MT models fine-tuned end-to-end on the silver data; and (3) FewShot—few-shot LLM prompting comparing 4 LLMs. We explore different prompting strategies and show that our best prompting strategy performs the best on all 9 languages, discussing the relative performance of the three approaches on Low vs High Resource languages and on in- vs out-of-domain data.The models, the test set, and the silver training data are available at https://github.com/MeloS7/Multilingual-KG-Verbalisation.",
@@ -107393,7 +107393,7 @@
     "id": "2025.emnlp-main.912",
     "title": "From Surveys to Narratives: Rethinking Cultural Value Adaptation in LLM s",
     "authors": [
-      "Farid Adilazuarda",
+      "M. Farid Adilazuarda",
       "Chen Cecilia Liu",
       "Iryna Gurevych",
       "Alham Fikri Aji"
@@ -168161,9 +168161,9 @@
     "id": "2025.emnlp-main.399",
     "title": "XA uto LM : Efficient Fine-Tuning of Language Models via Meta-Learning and A uto ML",
     "authors": [
-      "Ernesto Luis Estevanell Valladares",
-      "Suilan Estevez-Velarde",
-      "Yoan Gutierrez",
+      "Ernesto L. Estevanell-Valladares",
+      "Suilan Estévez-Velarde",
+      "Yoan Gutiérrez",
       "Andrés Montoyo",
       "Ruslan Mitkov"
     ],