Update README.md

README.md

@@ -84,6 +84,28 @@ The translation of the sentences was carried out on November 26th, 2023.
 **BibTeX:**
 
 ````BibTeX
+@inproceedings{skandalis-etal-2024-new-datasets,
+    title = "New Datasets for Automatic Detection of Textual Entailment and of Contradictions between Sentences in {F}rench",
+    author = "Skandalis, Maximos  and
+      Moot, Richard  and
+      Retor{\'e}, Christian  and
+      Robillard, Simon",
+    editor = "Calzolari, Nicoletta  and
+      Kan, Min-Yen  and
+      Hoste, Veronique  and
+      Lenci, Alessandro  and
+      Sakti, Sakriani  and
+      Xue, Nianwen",
+    booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
+    month = may,
+    year = "2024",
+    address = "Torino, Italy",
+    publisher = "ELRA and ICCL",
+    url = "https://aclanthology.org/2024.lrec-main.1065",
+    pages = "12173--12186",
+    abstract = "This paper introduces DACCORD, an original dataset in French for automatic detection of contradictions between sentences. It also presents new, manually translated versions of two datasets, namely the well known dataset RTE3 and the recent dataset GQNLI, from English to French, for the task of natural language inference / recognising textual entailment, which is a sentence-pair classification task. These datasets help increase the admittedly limited number of datasets in French available for these tasks. DACCORD consists of 1034 pairs of sentences and is the first dataset exclusively dedicated to this task and covering among others the topic of the Russian invasion in Ukraine. RTE3-FR contains 800 examples for each of its validation and test subsets, while GQNLI-FR is composed of 300 pairs of sentences and focuses specifically on the use of generalised quantifiers. Our experiments on these datasets show that they are more challenging than the two already existing datasets for the mainstream NLI task in French (XNLI, FraCaS). For languages other than English, most deep learning models for NLI tasks currently have only XNLI available as a training set. Additional datasets, such as ours for French, could permit different training and evaluation strategies, producing more robust results and reducing the inevitable biases present in any single dataset.",
+}
+
 @inproceedings{marelli-etal-2014-sick,
     title = "A {SICK} cure for the evaluation of compositional distributional semantic models",
     author = "Marelli, Marco  and
@@ -110,25 +132,11 @@ The translation of the sentences was carried out on November 26th, 2023.
     pages = "216--223",
     abstract = "Shared and internationally recognized benchmarks are fundamental for the development of any computational system. We aim to help the research community working on compositional distributional semantic models (CDSMs) by providing SICK (Sentences Involving Compositional Knowldedge), a large size English benchmark tailored for them. SICK consists of about 10,000 English sentence pairs that include many examples of the lexical, syntactic and semantic phenomena that CDSMs are expected to account for, but do not require dealing with other aspects of existing sentential data sets (idiomatic multiword expressions, named entities, telegraphic language) that are not within the scope of CDSMs. By means of crowdsourcing techniques, each pair was annotated for two crucial semantic tasks: relatedness in meaning (with a 5-point rating scale as gold score) and entailment relation between the two elements (with three possible gold labels: entailment, contradiction, and neutral). The SICK data set was used in SemEval-2014 Task 1, and it freely available for research purposes.",
 }
-
-@inproceedings{tiedemann-thottingal-2020-opus,
-    title = "{OPUS}-{MT} {--} Building open translation services for the World",
-    author = {Tiedemann, J{\"o}rg  and
-      Thottingal, Santhosh},
-    booktitle = "Proceedings of the 22nd Annual Conference of the European Association for Machine Translation",
-    month = nov,
-    year = "2020",
-    address = "Lisboa, Portugal",
-    publisher = "European Association for Machine Translation",
-    url = "https://aclanthology.org/2020.eamt-1.61",
-    pages = "479--480",
-    abstract = "This paper presents OPUS-MT a project that focuses on the development of free resources and tools for machine translation. The current status is a repository of over 1,000 pre-trained neural machine translation models that are ready to be launched in on-line translation services. For this we also provide open source implementations of web applications that can run efficiently on average desktop hardware with a straightforward setup and installation.",
-}
 ````
 
 **ACL:**
 
-Maximos Skandalis, Richard Moot, Christian Retoré, and Simon Robillard. 2024. *New datasets for automatic detection of textual entailment and of contradictions between sentences in French*. The 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), Turin, Italy. European Language Resources Association (ELRA) and International Committee on Computational Linguistics (ICCL).
+Maximos Skandalis, Richard Moot, Christian Retoré, and Simon Robillard. 2024. [New Datasets for Automatic Detection of Textual Entailment and of Contradictions between Sentences in French](https://aclanthology.org/2024.lrec-main.1065). In *Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)*, pages 12173–12186, Torino, Italy. ELRA and ICCL.
 
 And