Croatia

Treebanks annotated with Universal Dependencies (UD) are currently available for over 100 languages and are widely utilized by the community. However, their inherent characteristics are hard to measure and are only partially reflected in parser evaluations via accuracy metrics like LAS. In this study, we analyze a large subset of the UD treebanks using three recently proposed accuracy-free dataset analysis methods: dataset cartography, \$\textbackslash{}mathcal\{V\}\$-information, and minimum description length. Each method provides insights about UD treebanks that would remain undetected if only LAS was considered. Specifically, we identify a number of treebanks that, despite yielding high LAS, contain very little information that is usable by a parser to surpass what can be achieved by simple heuristics. Furthermore, we make note of several treebanks that score consistently low across numerous metrics, indicating a high degree of noise or annotation inconsistency present therein.

EACL 2023

Investigating UD Treebanks via Dataset Difficulty Measures

universal dependencies

dependency parsing

evaluation

stages 

COLING, the International Conference on Computational Linguistics, is one of the premier conferences for natural language processing and computational linguistics. Often grouped within the field of artificial intelligence, but actually pre-dating the development of artificial intelligence, advances in computational linguistics and natural language processing are now some of the major drivers behind the use of artificial intelligence for commercial and social applications – for example, on-line search, machine translation and with voice-assisted conversational devices.

First established in 1965, the biennial COLING conference is held in diverse parts of the globe and attracts participants from both top-ranked research centers and emerging countries. Today, the most important developments in our field are taking place not only in universities and academic research institutes, but also in industrial research departments and in technological startups. COLING conferences provide opportunities for all these communities to showcase their exciting developments.

COLING 2020

COLING, the International Conference on Computational Linguistics, is one of the premier conferences for natural language processing and computational linguistics. Often grouped within the field of artificial intelligence, but actually pre-dating the development of artificial intelligence, advances in computational linguistics and natural language processing are now some of the major drivers behind the use of artificial intelligence for commercial and social applications – for example, on-line search, machine translation and with voice-assisted conversational devices.

technical paper

### Welcome to the 17th Conference of the European Chapter of the Association for Computational Linguistics (EACL) 
Continuing its mission of expanding and involving the science community of all European countries, EACL had selected the Ukrainian community for the 16th EACL, which took place online due to the COVID pandemic. Unfortunately, the ongoing war made the organisation in Kyiv impossible. Considering the importance of physical interaction among researchers, especially after the restrictions imposed by the COVID pandemic, in addition to an online mode, the conference will be held in Dubrovnik, Croatia, from 2 to 6 of May, 2023. The original aim of strengthening the connection with the Ukrainian community will not change as our program will feature a dedicated session and a workshop to highlight work on Ukrainian language technologies. 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.

You need to register for the conference in order to access this site. Please visit https://2023.eacl.org/registration for more information.

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.

Representation of coreferential relations is a challenging and actively studied topic for pro-drop and morphologically rich languages (PD-MRLs) due to dropped pronouns (e.g., null subjects and omitted possessive pronouns). These phenomena require a representation scheme at the morphology level and enhanced evaluation methods. In this paper, we propose a representation \& evaluation scheme to incorporate dropped pronouns into coreference resolution and validate it on the Turkish language. Using the scheme, we extend the annotations on the only existing Turkish coreference dataset, which originally did not contain annotations for dropped pronouns. We provide publicly available pre and post processors to enhance the prominent CoNLL coreference scorer also to cover coreferential relations arising from dropped pronouns. As a final step, the paper reports the first neural Turkish coreference resolution results in the literature. Although validated on Turkish, the proposed scheme is language-independent and may be used for other PD-MRLs.

Incorporating Dropped Pronouns into Coreference Resolution: The case for Turkish

Automatic code generation from natural language descriptions can be highly beneficial during the process of software development. In this work, we propose GAP-Gen, a Guided Automatic Python Code Generation method based on Python syntactic constraints and semantic constraints. We first introduce Python
syntactic constraints in the form of Syntax-Flow, which is a simplified version of Abstract Syntax Tree (AST) reducing the size and high complexity of real Python AST but maintaining crucial syntactic information of Python code. In addition to Syntax-Flow, we introduce Variable-Flow which abstracts variable and function names consistently throughout the code. In our work, rather than pertaining, we focus on modifying the fine-tuning process which reduces computational requirements but retains high generation performance on automatic Python code generation task. GAP-Gen fine-tunes the transformer-based language models T5 and CodeT5 using
the Code-to-Docstring datasets CodeSearchNet, CodeSearchNet AdvTest and Code-Docstring-Corpus from EdinburghNLP. Our experiments show that GAP-Gen achieves better results on automatic Python code generation task than previous
works.

GAP-Gen: Guided Automatic Python Code Generation

Recent task-oriented dialogue systems are trained on annotated dialogues, which, in turn, reflect certain domain information (e.g., restaurants or hotels in a given region). However, when such domain knowledge changes (e.g., new restaurants open), the initial dialogue model may become obsolete, decreasing the overall performance of the system. Through a number of experiments, we show, for instance, that adding 50% of new slot-values reduces of about 55% the dialogue state-tracker performance. In light of such evidence, we suggest that automatic adaptation of training dialogues is a valuable option for re-training obsolete models. We experimented with a dialogue adaptation approach based on fine-tuning a generative language model on domain changes, showing that a significant reduction of performance decrease can be obtained.

Addressing Domain Changes in Task-oriented Conversational Agents through Dialogue Adaptation

Dealing with humor is an important step to develop Natural Language Processing tools capable of handling sophisticated semantic and pragmatic knowledge. In this context, this PhD thesis focuses on the automatic generation and recognition of verbal punning humor in Portuguese, which is still an underdeveloped language when compared to English. One of the main goals of this research is to conciliate Natural Language Generation computational models with existing theories of humor from the Humanities while avoiding mere generation by including contextual information into the generation process. Another point that is of utmost importance is the inclusion of the listener as an active part in the process of understanding and creating humor; we hope to achieve this by using concepts from Recommender Systems in our methods. Ultimately, we want to not only advance the current state-of-the-art in humor generation and recognition, but also to help the general Portuguese-speaking research community with methods, tools and resources that may aid in the development of further techniques for this language. We also expect our systems to provide insightful ideas about how humor is created and perceived by both humans and machines.

Towards Generation and Recognition of Humorous Texts in Portuguese

Citation screening is an essential yet time-consuming step of the systematic review process. Screening automation using machine learning techniques can speed up constructing a systematic review. Current efforts in automation are limited in their capabilities as they either only suggest the decisions or require large annotated datasets before making the predictions. In this PhD thesis, we propose several approaches to improving the screening automation techniques to reduce reviewers' work. First, we want to propose a unified benchmarking method for the citation screening task. We will then explore different machine learning algorithms for conducting screening, focusing on using fewer annotated examples to maintain stable results across multiple reviews. Next, we intend to experiment with using external information coming from eligibility criteria sheets as a substitute for manual annotations. Finally, we would like to assess our approaches to literature reviews outside of the medical domain.

Rapid Systematic Reviews: Zero-shot Citation Screening with the Usage of Eligibility Criteria

Examining a sample of Instant Messaging (IM) dialogues by conventional, manual methods, often used in social sciences, is difficult and inefficient for two reasons: a sample of significant size comprises a large quantity of unstructured text, and additionally, the occurrence of researched phenomena tends to be low. Therefore, automatic detection of particular psychological phenomena would enable the research of such data sources on a much larger scale than before. It will be the primary goal of the dissertation thesis to explore the possible Natural Language Processing (NLP) approaches to the problem and report their effectiveness. This proposal provides the theoretical basis by researching state-of-the-art solutions for related tasks, formulates hypotheses for the possible solutions, and presents the already achieved results. The proposed hypotheses will be verified in the dissertation thesis by designing, training, and evaluating predictive models. The resulting work will provide a comprehensive overview of different methods and their effectiveness. To the best of the author's knowledge, this thesis will be the first work to do so in this area on real IM dialogues provided by adolescents. The results could be used, for example, in parental applications, mental health applications that supervise users' well-being, risky online behavior detection, intent detection for chatbots, and many others. This proposal also describes a research plan for conducting the research and publishing the results.

Detecting Psychological Phenomena in Dialogues

Clinical natural language processing aims to tackle language and prediction tasks using text from medical practice, such as clinical notes, prescriptions, and discharge summaries. Several approaches have been tried to deal with these tasks. Since 2017, pre-trained language models (PLMs) have achieved state-of-the-art performance in many tasks. However, most works have been developed in English. This PhD research proposal addresses the development of PLMs for clinical NLP in Spanish. To carry out this study, we will build a clinical corpus big enough to implement a functional PLM. We will test several PLM architectures and evaluate them with language and prediction tasks. The novelty of this work lies in the use of only clinical text, while previous clinical PLMs have used a mix of general, biomedical, and clinical text.

Development of pre-trained language models for clinical NLP in Spanish

Generating polite responses is essential to build intelligent and engaging dialogue systems. However, this task is far from well-explored due to the difficulties of rendering a particular style in coherent responses, especially when parallel datasets for regular-to-polite pairs are usually unavailable. This paper proposes a polite chatbot that can produce responses that are polite and coherent to the given context. In this study, a politeness transfer model is first used to generate polite synthetic dialogue pairs of contexts and polite utterances. Then, these synthetic pairs are employed to train a dialogue model. Automatic and human evaluations demonstrate that our method outperforms baselines in producing polite dialogue responses while staying competitive in terms of coherent to the given context.

Polite Chatbot: A Text Style Transfer Application

Writing is an important element of language learning, and an increasing amount of learner writing is taking place in online environments. Teachers can provide valuable feedback by commenting on learner text. However, providing relevant feedback for every issue for every student can be time-consuming. To address this, we turn to the NLP subfield of feedback comment generation, the task of automatically generating explanatory notes for learner text with the goal of enhancing learning outcomes. 
However, freely-generated comments may mix multiple topics seen in the training data or even give misleading advice. In this thesis proposal, we seek to address these issues by categorizing comments and constraining the outputs of noisy classes. We describe an annotation scheme for feedback comment corpora using comment topics with a broader scope than existing typologies focused on error correction. We outline plans for experiments in grouping and clustering, replacing particularly diverse categories with modular templates, and comparing the generation results of using different linguistic features and model architectures with the original dataset versus the newly annotated one. This paper presents the first two years (the master's component) of a research project for a five-year combined master's and Ph.D program.

Template-guided Grammatical Error Feedback Comment Generation

While text-based medical applications have become increasingly prominent, access to clinical data remains a major concern. To resolve this issue, further de-identification and anonymization of the data are required. This might, however, alter the contextual information within the clinical texts and therefore influence the learning and performance of possible language models. 
This paper systematically analyses the potential effects of various anonymization techniques on the performance of state-of-the-art machine learning models based on several datasets corresponding to five different NLP tasks.
On this basis, we derive insightful findings and recommendations concerning text anonymization with regard to the performance of machine learning models. In addition, we present a simple re-identification attack applied to the anonymized text data, which can break the anonymization.

Downloads

Next from EACL 2023

Incorporating Dropped Pronouns into Coreference Resolution: The case for Turkish

Similar lecture

Scalable Cross-lingual Treebank Synthesis for Improved Production Dependency Parsers