Eacl 2023

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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,

Eacl 2023

Xu Graham Neubig. Rojas Barahona. Lee Jason Lee. Is this true? Hiroshi Noji Yohei Oseki. Data from experiments on three tasks, five datasets, and six models with four attacks show that punctuation insertions, when limited to a few symbols apostrophes and hyphens , are a superior attack vector compared to character insertions due to 1 a lower after-attack accuracy A aft-atk than alphabetical character insertions; 2 higher semantic similarity between the resulting and original texts; and 3 a resulting text that is easier and faster to read as assessed with the Test of Word Reading Efficiency TOWRE. Our findings indicate that inserting a few punctuation types that result in easy-to-read samples is a general attack mechanism. While multimodal sentiment analysis MSA has gained much attention over the last few years, the main focus of most work on MSA has been limited to constructing multimodal representations that capture interactions between different modalities in a single task. This was largely due to a lack of unimodal annotations in MSA benchmark datasets. However, training a model using only multimodal representations can lead to suboptimal performance due to insufficient learning of each uni-modal representation. In this work, to fully optimize learning representations from multimodal data, we propose SUGRM which jointly trains multimodal and unimodal tasks using recalibrated features. The features are recalibrated such that the model learns to weight the features differently based on the features of other modalities. Further, to leverage unimodal tasks, we auto-generate unimodal annotations via a unimodal label generation module ULGM. The experiment results on two benchmark datasets demonstrate the efficacy of our framework.

We quantify the sensitivity of the model to structural complexity and distinguish a range of eacl 2023 characteristics. We describe MultiFin — a publicly available financial dataset consisting of real-world article headlines covering 15 languages across different writing systems and language families. February 27, eacl 2023,

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. Note: The authors can withdraw their paper from ARR by October 13, , regardless of how many reviews it has received.

February 28, The handbook of EACL is now available at this link. It will be updated once the full program is finalized. February 21, Also, there are still a few rooms available at the Radisson. February 12, February 11,

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,

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Unfortunately, the ongoing war made the organisation in Kyiv impossible. A direct quote enclosed in quotation marks has a strong visual appeal and is a sign of a reliable citation. We focussed on metaphors that can serve asregister markers and can also be reliably indentifiedfor annotation. To facilitate future research directions, we will make the dataset and the code publicly available upon publication. We propose that training on smaller amounts of data but from related languages could match the performance of models trained on large, unrelated data. Though we observe several high-quality long-sequence datasets for English and other monolingual languages, there is no significant effort in building such resources for code-mixed languages such as Hinglish code-mixing of Hindi-English. EACL encourages the submission of these supplementary materials to improve the reproducibility of results, and to enable authors to provide additional information that does not fit in the paper. Our selection method with proper hyperparameters yields better parsing performance than the other baselines on two multilingual datasets. The second method uses a varying number of soft prompt tokens to encourage language models to learn different prompts. Recent years have witnessed increasing interests in prompt-based learning in which models can be trained on only a few annotated instances, making them suitable in low-resource settings. The CALL complements shortcomings that may occur when utilizing a calibration method individually and boosts both classification and calibration accuracy.

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The second stage is pseudo label learning in which the model is re-trained with pseudo-labels obtained in the first stage. In addition, limitations such as low scalability to long text, the requirement of large GPU resources, or other things that inspire crucial further investigation are welcome. Since the pre-training for PLMs does not consider summarization-specific information such as the target summary length, there is a gap between the pre-training and fine-tuning for PLMs in summarization tasks. Additionally, we share a strong multi-task baseline model which outperforms single-task fine-tuned models on the CALM-Bench tasks. We also highlight shortcomings of existing evaluation methods, and introduce new metrics that take into account both lexical and high-level semantic similarity. The ethics statement will not count toward the page limit 8 pages for long, 4 pages for short papers. A human evaluation conducted on a random sample of the test set further establishes the effectiveness of the proposed approach. You can find answers to frequently asked questions in the new FAQ section, which will be continually updated. The experiments and analyses demonstrate the effectiveness of iNLG on open-ended text generation tasks, including text completion, story generation, and concept-to-text generation in both few-shot and full-data scenarios. In this work, we propose a summarize-then-simplify two-stage strategy, which we call NapSS, identifying the relevant content to simplify while ensuring that the original narrative flow is preserved. In addition to acceptance or rejection, papers may receive a conditional acceptance recommendation. Experiments on two public benchmarks show that PTS achieves 3. We recommend selecting training data based on language-relatedness when pretraining language models for low-resource languages.