Eacl 2023

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However, in order to keep the review load on the community as a whole manageable, we ask authors to decide up-front if they want their papers to be reviewed through ARR or EACL. Note: Submissions from ARR cannot be modified except that they can be associated with an author response. Consequently, care must be taken in deciding whether a submission should be made to ARR or EACL directly if the work has not been submitted anywhere before the call. Plan accordingly. This means that the submission must either be explicitly withdrawn by the authors, or the ARR reviews are finished and shared with the authors before October 13, , and the paper was not re-submitted to ARR.

Eacl 2023

The hotel venue lost Internet due construction nearby. The plenary Keynote is being recorded for you to view later. May 4, Awards for Best Paper and Outstanding Paper can be viewed here. Congratulations to the winners! May 1, The conference handbook download link is now available, providing a brief overview of the important aspects of the programme. April 15, The list of accepted volunteers is now available here! Please, make sure to confirm your participation by e-mail in case of acceptance! March 17, The registration for EACL is now open, check the registration page for more details! February 27, The accepted tutorials are now available online!

To overcome these challenges, we propose a domain-agnostic extractive question answering QA approach with eacl 2023 weights across domains. Searching troves of videos with textual descriptions is a core multimodal retrieval task.

Xu Graham Neubig. Rojas Barahona. Lee Jason Lee. Is this true? Hiroshi Noji Yohei Oseki.

The hotel venue lost Internet due construction nearby. The plenary Keynote is being recorded for you to view later. May 4, Awards for Best Paper and Outstanding Paper can be viewed here. Congratulations to the winners!

Eacl 2023

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We hope that this new resource paves the way for further research in generalization of neural reasoning models in Dutch, and contributes to the development of better language technology for Natural Language Inference, specifically for Dutch. Large-scale, high-quality corpora are critical for advancing research in coreference resolution. Prompt-based learning methods in semi-supervised learning SSL settings have been shown to be effective on multiple natural language understanding NLU datasets and tasks in the literature. On carefully analyzing the remaining disagreements, we identify the presence of linguistic cases that our annotators unanimously agree upon but lack unified treatments e. Our technique has important applications — one of them is investigative journalism, where automatically extracting conflict-of-interest between scientists and funding organizations helps understand the type of relations companies engage with the scientists. In this paper, we present an empirical study on confidence calibration for PLMs, addressing three categories, including confidence penalty losses, data augmentations, and ensemble methods. This vector generates soft prompts, via a lightweight prompt generator, which modulates a frozen model. A novel feature represents a cluster of semantically equivalent novel user requests e. We fine-tune and evaluate our model on three important natural language downstream tasks, Part-of-speech tagging, Named-entity recognition, and Question answering. This inconsistent behavior during model upgrade often outweighs the benefits of accuracy gain and hinders the adoption of new models. We find that leveraging metaphor improves model performance, particularly for the two most common propaganda techniques: loaded language and name-calling. The empirical results demonstrate that ViDeBERTa with far fewer parameters surpasses the previous state-of-the-art models on multiple Vietnamese-specific natural language understanding tasks. However, for more complex tasks such as dialogue state tracking DST , designing prompts that reliably convey the desired intent is nontrivial, leading to unstable results. Federated learning with pretrained language models for language tasks has been gaining attention lately but there are definite confounders that warrants a careful study.

Julians, Malta, from 17 to 22 of March, As the flagship European conference in the field of computational linguistics, EACL welcomes European and international researchers covering a broad spectrum of research areas that are concerned with computational approaches to natural language. Many of the initial motivations for the foundation of EACL are no longer relevant, mainly due to the Internet, online banking, and the transformation of ACL from an American to an international organisation in

Extensive experiments show that our model outperforms or achieves competitive performance when compared to previous state-of-the-art algorithms in the following settings: rich-resource, cross-domain transferability, few-shot supervision, and segmentation when topic label annotations are provided. However, the source of this improvement is yet unclear. Experimental results show that the proposed method outperforms competitive baseline models on all automatic and human evaluation metrics. We propose KNN-Former, which incorporates a new kind of spatial bias in attention calculation based on the K-nearest-neighbor KNN graph of document entities. Wherever appropriate, concrete evaluation and analysis should be included. Please read the ethics FAQ for more guidance on some problems to look out for and key concerns to consider relative to the code of ethics. Experimental results on three QA benchmarks show that our method significantly outperforms previous closed-book QA methods. We first generate a related context for a given question by prompting a pretrained LM. To facilitate future research into these types of documents, we release a new ID document dataset that covers diverse templates and languages. We present a new approach in which large language models are utilized to generate source documents that allow predicting, given a high-level event definition, the specific events, arguments, and relations between them to construct a schema that describes the complex event in its entirety. We are interested in the relation betweenmetaphor and register, hence, the corpusincludes material from different registers. Citation count prediction is the task of predicting the future citation counts of academic papers, which is particularly useful for estimating the future impacts of an ever-growing number of academic papers. We argue that recurrent syntactic and semantic regularities in textual data can be used to provide the models with both structural biases and generative factors. We demonstrate how the proposed dataset can be used to model the task of multimodal summarization by training a Transformer-based neural model. The Call for Demos is out!