Croatia

The task of Grammatical Error Correction (GEC) has been extensively studied in the English language. However, its application to low-resource languages, such as Ukrainian, remains an open challenge. In this paper, we develop sequence tagging and neural machine translation models for the Ukrainian language as well as a
set of algorithmic correction rules to augment those systems. We also develop synthetic data generation techniques for the Ukrainian language to create high-quality human-like errors. Finally, we determine the best combination of synthetically generated data to augment the existing UA-GEC corpus and achieve the state-of-the-art results of 0.663 F0.5 score on the newly established UA-GEC benchmark. The code and trained models are publicly available on GitHub and HuggingFace.

EACL 2023

Comparative Study of Models Trained on Synthetic Data for Ukrainian Grammatical Error Correction

ukrainian

workshop 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.

We present our system that participated in the
shared task on the grammatical error correction
of Ukrainian. We have implemented two ap-
proaches that make use of large pre-trained lan-
guage models and synthetic data, that have been
used for error correction of English as well as
low-resource languages. The first approach is
based on finetuning a large multilingual lan-
guage model (mT5) in two stages: first, on
synthetic data, and then on gold data. The sec-
ond approach trains a (smaller) seq2seq Trans-
former model pre-trained on synthetic data and
finetuned on gold data. Our mT5-based model
scored first in “GEC only” track, and a very
close second in the “GEC+Fluency” track. Our
two key innovations are (1) finetuning in stages,
first on synthetic, and then on gold data; and
(2) a high-quality corruption method based on
round-trip machine translation to complement
existing noisification approaches.

A Low-Resource Approach to the Grammatical Error Correction of Ukrainian

This paper presents the results of the UNLP 2023 shared task, the first Shared Task on Grammatical Error Correction for the Ukrainian language. The task included two tracks: GEC-only and GEC+Fluency. The dataset and evaluation scripts were provided to the participants, and the final results were evaluated on a hidden test set. Six teams submitted their solutions before the deadline, and four teams submitted papers that were accepted to appear in the UNLP workshop proceedings and are referred to in this report. The CodaLab leaderboard is left open for further submissions.

The UNLP 2023 Shared Task on Grammatical Error Correction for Ukrainian

For monitoring crises, political events are extracted from the news. The large amount of unstructured full-text event descriptions makes a case-by-case analysis unmanageable, particularly for low-resource humanitarian aid organizations. This creates a demand to classify events into event types, a task referred to as event coding. Typically, domain experts craft an event type ontology, annotators label a large dataset and technical experts develop a supervised coding system. In this work, we propose PR-ENT, a new event coding approach that is more flexible and resource-efficient, while maintaining competitive accuracy.

Rethinking the Event Coding Pipeline with Prompt Entailment

An approach to improve question-answering performance is to retrieve accompanying information that contains factual evidence matching the question. These retrieved documents are then fed into a reader that generates an answer. A commonly applied retriever is dense passage retrieval. In this retriever, the output of a transformer neural network is used to query a knowledge database for matching documents. Inspired by the observation that different layers of a transformer network provide rich representations with different levels of abstraction, we hypothesize that useful queries can be generated not only at the output layer, but at every layer of a transformer network, and that the hidden representations of different layers may combine to improve the fetched documents for reader performance. Our novel approach integrates retrieval into each layer of a transformer network, exploiting the hierarchical representations of the input question. We show that our technique outperforms prior work on downstream tasks such as question answering, demonstrating the effectiveness of our approach.

Hierarchical Representations in Dense Passage Retrieval for Question-Answering

Existing fact-checking models for biomedical claims are typically trained on synthetic or well-worded data and hardly transfer to social media content. This mismatch can be mitigated by adapting the social media input to mimic the focused nature of common training claims. To do so, Wuehrl & Klinger (2022) propose to extract concise claims based on medical entities in the text. However, their study has two limitations: First, it relies on gold-annotated entities. Therefore, its feasibility for a real-world application cannot be assessed since this requires detecting relevant entities automatically. Second, they represent claim entities with the original tokens. This constitutes a terminology mismatch which potentially limits the fact-checking performance. To understand both challenges, we propose a claim extraction pipeline for medical tweets that incorporates named entity recognition and terminology normalization via entity linking. We show that automatic NER does lead to a performance drop in comparison to using gold annotations but the fact-checking performance still improves considerably over inputting the unchanged tweets. Normalizing entities to their canonical forms does, however, not improve the performance.

An Entity-based Claim Extraction Pipeline for Real-world Biomedical Fact-checking

Modern fact verification systems have distanced themselves from the black box paradigm by providing the evidence used to infer their veracity judgments. Hence, evidence-backed fact verification systems' performance heavily depends on the capabilities of their retrieval component to identify these facts. A popular evaluation benchmark for these systems is the FEVER task, which consists of determining the veracity of short claims using sentences extracted from Wikipedia. In this paper, we present a novel approach to the the retrieval steps of the FEVER task leveraging the graph structure of Wikipedia. The retrieval models surpass state of the art results at both sentence and document level. Additionally, we show that by feeding our retrieved evidence to the best-performing textual entailment model, we set a new state of the art in the FEVER competition.

Enhancing Information Retrieval in Fact Extraction and Verification

Recently it has been shown that without any access to the contextual passage, multiple choice reading comprehension (MCRC) systems are able to answer questions significantly better than random on average. These systems use their accumulated "world knowledge" to directly answer questions, rather than using information from the passage. This paper examines the possibility of exploiting this observation as a tool for test designers to ensure that the form of "world knowledge" is acceptable for a particular set of questions. We propose information-theory based metrics that enable the level of "world knowledge" exploited by systems to be assessed. Two metrics are described: the expected number of options, which measures whether a passage-free system can identify the answer a question using world knowledge; and the contextual mutual information, which measures the importance of context for a given question. We demonstrate that questions with low expected number of options, and hence answerable by the shortcut system, are often similarly answerable by humans without context. This highlights that the general knowledge 'shortcuts' could be equally used by exam candidates, and that our proposed metrics may be helpful for future test designers to monitor the quality of questions.

"World Knowledge" in Multiple Choice Reading Comprehension

Automatic fact verification has become an increasingly popular topic in recent years and among datasets the Fact Extraction and VERification (FEVER) dataset is one of the most popular. In this work we present BEVERS, a tuned baseline system for the FEVER dataset. Our pipeline uses standard approaches for document retrieval, sentence selection, and final claim classification, however, we spend considerable effort ensuring optimal performance for each component. The results are that BEVERS achieves the highest FEVER score and label accuracy among all systems, published or unpublished. We also apply this pipeline to another fact verification dataset, Scifact, and achieve the highest label accuracy among all systems on that dataset as well. We also make our full code available.

BEVERS: A General, Simple, and Performant Framework for Automatic Fact Verification

Explanation prompts ask language models to not only assign a label to a given input, such as entailment or contradiction in natural language inference (NLI) tasks, but also to generate a free-text explanation that supports this label. While explanation prompts were originally introduced aiming to improve model interpretability, here we show that they also improve robustness to superficial cues. Compared to prompting for labels only, explanation prompt- ing shows stronger performance on adversarial NLI benchmarks, outperforming state of the art on ANLI, Counterfactually-Augmented NLI, and SNLI-Hard datasets. Analysis suggests that the increase in robustness is due to a reduction in the association strength be- tween single tokens and labels, i.e., explanation prompting weakens superficial cues. More specifically, we find that single tokens that are highly predictive of the correct answer in the label-only setting become uninformative when the model also has to generate explanations.

Prompting for explanations improves Adversarial NLI. Is this true? {Yes} it is {true} because {it weakens superficial cues}

In my talk I will discuss bias recognition on BASIL, a dataset of political news which has been annotated with different kinds of biases. The start of the presentation unveils several inconsistencies in prior work using this dataset, showing that most approaches focus only on certain task formulations while ignoring others, and also failing to report important evaluation details. Later I discuss a comprehensive categorization of these approaches, as well as a more uniform and clear set of evaluation metrics. In the end, the importance of the missing formulations and propose the novel task of simultaneously detecting different kinds of biases in news is discussed and various task formulations on six different BASIL classification tasks provide a unified manner for detecting bias in general.

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