Eacl 2023

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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.

Eacl 2023

Have you checked our knowledge base? Documentation Contact Us Sign up Log in. Conference Paper Two-column. The document itself conforms to its own specifications, and is, therefore, an example of what your manuscript should look like. These instructions should be used both for papers submitted for review and for final versions of accepted papers. They are not self-contained. The style is based on the natbib package and supports all natbib citation commands. It also supports commands defined in previous ACL style files for compatibility. By default, the box containing the title and author names is set to the minimum of 5 cm. Do not set this length smaller than 5 cm. We encourage you to use the natbib styles. To include both the Anthology and your own. This section should occur after the conclusion, but before the references. It will not count towards the page limit.

You can find out more in our cookie policy. Given an instance, eacl 2023, its embedding queries the latent space, eacl 2023 a basis skill vector. In this paper, we propose a novel training-free compression method for multi-task language models using pruning method.

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 This means that the submission must either be explicitly withdrawn by the authors, or the ARR reviews are finished and shared with the authors before October 13, , and the paper was not re-submitted to ARR.

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Eacl 2023

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In this work, we propose to learn a shared latent space which captures a set of basis skills from a mixture of source tasks. This includes various dimensions of conversation quality with human evaluation directly on the synthesized conversations, and interactive human evaluation of chatbots fine-tuned on the synthetically generated dataset. We believe that LLMs can serve as a simple yet generic baseline for future research. 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. Reinforcement learning has been widely adopted to model dialogue managers in task-oriented dialogues. Note: The authors can withdraw their paper from ARR by October 13, , regardless of how many reviews it has received. Detecting and supporting novel features is crucial towards wider adoption of dialog systems by end users. While pre-trained language models PLMs have become a de-facto standard promoting the accuracy of text classification tasks, recent studies find that PLMs often predict over-confidently. However, extracting life events from conversations is rarely explored. However, you are expected to mention such works in your submission, and list their published results if they are directly relevant. To disentangle the complex domain information in ToDs, we train our DST with a novel domain filtering strategy by excluding out-of-domain question samples. Theoretically, our model provides computational evidence for the learnability of usage-based constructionist theories of language acquisition.

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In this work, we introduce feature discovery as opposed to single intent discovery, which aims at discovering novel features spanning a combination of intents and slots, and present a technique for discovering novel features from user utterances. Practically, the techniques that we present facilitate the learning of computationally tractable, usage-based construction grammars, which are applicable for natural language understanding and production tasks. While these two tasks are typically performed in succession, we study here Bayesian models for simultaneously segmenting utterances at these two levels. In addition, we will not consider any paper that overlaps significantly in content or results with papers that will be or have been published elsewhere. Commonsense generation aims to generate a plausible sentence containing all given unordered concept words. 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. Conversely, the mere inclusion of an ethical considerations section does not guarantee acceptance. Hiroshi Noji Yohei Oseki. Rather than operating on dialogue context alone, agents have access to hierarchical schemas containing task-relevant natural language descriptions. Since the task is new, we introduce an automatic evaluation strategy by measuring how much the generated questions via questionnaire are answerable to ensure factual correctness and covers the source meeting for the depth of possible exploration. Advances in natural language processing, such as transfer learning from pre-trained language models, have impacted how models are trained for programming language tasks too. Violations of the code of ethics as well as complaints raised under the anti-harassment policy should be brought to the Professional Conduct Committee , who can be reached during the conference via the PCC channel in RocketChat, or you may also contact any current member of the EACL Executive Board. As a result, users are supplied with a list of suggested questions reflecting their preferences. For example on TriviaQA, our method improves exact match accuracy from The latent space and prompt transformation are learned end-to-end by training on source tasks.