ymoslem/acl-abstracts huggingface dataset:
Improving Low-resource RRG Parsing with Structured Gloss Embeddings (2023)

Treebanking for local languages is hampered by the lack of existing parsers to generate pre-annotations. However, it has been shown that reasonably accurate parsers can be bootstrapped with little initial training data when use is made of the information in interlinear glosses and translations that language documentation data for such treebanks typically comes with. In this paper, we improve upon such a bootstrapping model by representing glosses using a combination of morphological feature vectors and pre-trained lemma embeddings. We also contribute a mapping from glosses to Universal Dependencies morphological features.
Approaches to Corpus Creation for Low-Resource Language Technology: the Case of Southern Kurdish and Laki (2023)

One of the major challenges that under-represented and endangered language communities face in language technology is the lack or paucity of language data. This is also the case of the Southern varieties of the Kurdish and Laki languages for which very limited resources are available with insubstantial progress in tools. To tackle this, we provide a few approaches that rely on the content of local news websites, a local radio station that broadcasts content in Southern Kurdish and fieldwork for Laki. In this paper, we describe some of the challenges of such under-represented languages, particularly in writing and standardization, and also, in retrieving sources of data and retro-digitizing handwritten content to create a corpus for Southern Kurdish and Laki. In addition, we study the task of language identification in light of the other variants of Kurdish and Zaza-Gorani languages.
AraDiaWER: An Explainable Metric For Dialectical Arabic ASR (2023)

Linguistic variability poses a challenge to many modern ASR systems, particularly Dialectical Arabic (DA) ASR systems dealing with low-resource dialects and resulting morphological and orthographic variations in text and speech. Traditional evaluation metrics such as the word error rate (WER) inadequately capture these complexities, leading to an incomplete assessment of DA ASR performance. We propose AraDiaWER, an ASR evaluation metric for Dialectical Arabic (DA) speech recognition systems, focused on the Egyptian dialect. AraDiaWER uses language model embeddings for the syntactic and semantic aspects of ASR errors to identify their root cause, not captured by traditional WER. MiniLM generates the semantic score, capturing contextual differences between reference and predicted transcripts. CAMeLBERT-Mix assigns morphological and lexical tags using a fuzzy matching algorithm to calculate the syntactic score. Our experiments validate the effectiveness of AraDiaWER. By incorporating language model embeddings, AraDiaWER enables a more interpretable evaluation, allowing us to improve DA ASR systems. We position the proposed metric as a complementary tool to WER, capturing syntactic and semantic features not represented by WER. Additionally, we use UMAP analysis to observe the quality of ASR embeddings in the proposed evaluation framework.
A Quest for Paradigm Coverage: The Story of Nen (2023)

Language documentation aims to collect a representative corpus of the language. Nevertheless, the question of how to quantify the comprehensive of the collection persists. We propose leveraging computational modelling to provide a supplementary metric to address this question in a low-resource language setting. We apply our proposed methods to the Papuan language Nen. Nen is actively in the process of being described and documented. Given the enormity of the task of language documentation, we focus on one subdomain, namely Nen verbal morphology. This study examines four verb types: copula, positional, middle, and transitive. We propose model-based paradigm generation for each verb type as a new way to measure completeness, where accuracy is analogous to the coverage of the paradigm. We contrast the paradigm attestation within the corpus (constructed from fieldwork data) and the accuracy of the paradigm generated by Transformer models trained for inflection. This analysis is extended by extrapolating from the learning curve established to provide predictions for the quantity of data required to generate a complete paradigm correctly. We also explore the correlation between high-frequency morphosyntactic features and model accuracy. We see a positive correlation between high-frequency feature combinations and model accuracy, but this is only sometimes the case. We also see high accuracy for low-frequency morphosyntactic features. Our results show that model coverage is significantly higher for the middle and transitive verbs but not the positional verb. This is an interesting finding, as the positional verb paradigm is the smallest of the four.
Multilingual Automatic Extraction of Linguistic Data from Grammars (2023)

One of the goals of field linguistics is compilation of descriptive grammars for relatively little-studied languages. Until recently, extracting linguistic characteristics from grammatical descriptions and creating databases based on them was done manually. The aim of this paper is to apply methods of multilingual automatic information extraction to grammatical descriptions written in different languages of the world: we present a search engine for grammars, which would accelerate the tedious and time-consuming process of searching for information about linguistic features and facilitate research in the field of linguistic typology.
Rethinking the Event Coding Pipeline with Prompt Entailment (2023)

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: first, we extend an event description such as {\textquotedblleft}Military injured two civilians{\textquotedblright} by a template, e.g. {\textquotedblleft}People were [Z]{\textquotedblright} and prompt a pre-trained (cloze) language model to fill the slot Z. Second, we select suitable answer candidates Zstar = {\textquotedblleft}injured{\textquotedblright}, {\textquotedblleft}hurt{\textquotedblright}... by treating the event description as premise and the filled templates as hypothesis in a textual entailment task. In a final step, the selected answer candidate can be mapped to its corresponding event type. This allows domain experts to draft the codebook directly as labeled prompts and interpretable answer candidates. This human-in-the-loop process is guided by our codebook design tool. We show that our approach is robust through several checks: perturbing the event description and prompt template, restricting the vocabulary and removing contextual information.
Hierarchical Representations in Dense Passage Retrieval for Question-Answering (2023)

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.
An Entity-based Claim Extraction Pipeline for Real-world Biomedical Fact-checking (2023)

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, W{\"u}hrl and Klinger (2022a) 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.
Enhancing Information Retrieval in Fact Extraction and Verification (2023)

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.
\textquotedblleftWorld Knowledge\textquotedblright in Multiple Choice Reading Comprehension (2023)

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 {\textquotedblleft}world knowledge{\textquotedblright} 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 {\textquotedblleft}world knowledge{\textquotedblright} is acceptable for a particular set of questions. We propose information-theory based metrics that enable the level of {\textquotedblleft}world knowledge{\textquotedblright} 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 {\textquoteleft}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.
BEVERS: A General, Simple, and Performant Framework for Automatic Fact Verification (2023)

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.
An Effective Approach for Informational and Lexical Bias Detection (2023)

In this paper we present a thorough investigation of automatic bias recognition on BASIL, a dataset of political news which has been annotated with different kinds of biases. We begin by unveiling 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. We provide a comprehensive categorization of these approaches, as well as a more uniform and clear set of evaluation metrics. We argue about the importance of the missing formulations and also propose the novel task of simultaneously detecting different kinds of biases in news. In our work, we tackle bias on six different BASIL classification tasks in a unified manner. Eventually, we introduce a simple yet effective approach based on data augmentation and preprocessing which is generic and works very well across models and task formulations, allowing us to obtain state-of-the-art results. We also perform ablation studies on some tasks to quantify the strength of data augmentation and preprocessing, and find that they correlate positively on all bias tasks.
WRF: Weighted Rouge-F1 Metric for Entity Recognition (2023)

The continuous progress in Named Entity Recognition allows the identification of complex entities in multiple domains. The traditionally used metrics like precision, recall, and F1-score can only reflect the classification quality of the underlying NER model to a limited extent. Existing metrics do not distinguish between a non-recognition of an entity and a misclassification of an entity. Additionally, the dealing with redundant entities remains unaddressed. We propose WRF, a Weighted Rouge F1 metric for Entity Recognition, to solve the mentioned gaps in currently available metrics. We successfully employ the WRF metric for automotive entity recognition, followed by a comprehensive qualitative and quantitative analysis of the obtained results.
Assessing Distractors in Multiple-Choice Tests (2023)

Multiple-choice tests are a common approach for assessing candidates' comprehension skills. Standard multiple-choice reading comprehension exams require candidates to select the correct answer option from a discrete set based on a question in relation to a contextual passage. For appropriate assessment, the distractor answer options must by definition be incorrect but plausible and diverse. However, generating good quality distractors satisfying these criteria is a challenging task for content creators. We propose automated assessment metrics for the quality of distractors in multiple-choice reading comprehension tests. Specifically, we define quality in terms of the incorrectness, plausibility and diversity of the distractor options. We assess incorrectness using the classification ability of a binary multiple-choice reading comprehension system. Plausibility is assessed by considering the distractor confidence - the probability mass associated with the distractor options for a standard multi-class multiple-choice reading comprehension system. Diversity is assessed by pairwise comparison of an embedding-based equivalence metric between the distractors of a question. To further validate the plausibility metric we compare against candidate distributions over multiple-choice questions and agreement with a ChatGPT model`s interpretation of distractor plausibility and diversity.
Delving into Evaluation Metrics for Generation: A Thorough Assessment of How Metrics Generalize to Rephrasing Across Languages (2023)

Language generation has been an important task in natural language processing (NLP) with increasing variety of applications especially in the recent years. The evaluation of generative language models typically rely on automatic heuristics which search for overlaps over word or phrase level patterns in generated outputs and traditionally some hand-crafted reference sentences in the given language ranging in the forms from sentences to entire documents. Language, on the other hand, is productive by nature, which means the same concept can be expressed potentially in many different lexical or phrasal forms, making the assessment of generated outputs a very difficult one. Many studies have indicated potential hazards related to the prominent choice of heuristics matching generated language to selected references and the limitations raised by this setting in developing robust generative models. This paper undertakes an in-depth analysis of evaluation metrics used for generative models, specifically investigating their responsiveness to various syntactic structures, and how these characteristics vary across languages with different morphosyntactic typologies. Preliminary findings indicate that while certain metrics exhibit robustness in particular linguistic contexts, a discernible variance emerges in their performance across distinct syntactic forms. Through this exploration, we highlight the imperative need for more nuanced and encompassing evaluation strategies in generative models, advocating for metrics that are sensitive to the multifaceted nature of languages.
EduQuick: A Dataset Toward Evaluating Summarization of Informal Educational Content for Social Media (2023)

This study explores the capacity of large language models (LLMs) to efficiently generate summaries of informal educational content tailored for platforms like TikTok. It also investigates how both humans and LLMs assess the quality of these summaries, based on a series of experiments, exploring the potential replacement of human evaluation with LLMs. Furthermore, the study delves into how experienced content creators perceive the utility of automatic summaries for TikTok videos. We employ strategic prompt selection techniques to guide LLMs in producing engaging summaries based on the characteristics of viral TikTok content, including hashtags, captivating hooks, storytelling, and user engagement. The study leverages OpenAI`s GPT-4 model to generate TikTok content summaries, aiming to align them with the essential features identified. By employing this model and incorporating human evaluation and expert assessment, this research endeavors to shed light on the intricate dynamics of modern content creation, where AI and human ingenuity converge. Ultimately, it seeks to enhance strategies for disseminating and evaluating educational information effectively in the realm of social media.
Zero-shot Probing of Pretrained Language Models for Geography Knowledge (2023)

Gauging the knowledge of Pretrained Language Models (PLMs) about facts in niche domains is an important step towards making them better in those domains. In this paper, we aim at evaluating multiple PLMs for their knowledge about world Geography. We contribute (i) a sufficiently sized dataset of masked Geography sentences to probe PLMs on masked token prediction and generation tasks, (ii) benchmark the performance of multiple PLMs on the dataset. We also provide a detailed analysis of the performance of the PLMs on different Geography facts.
Transformers Go for the LOLs: Generating (Humourous) Titles from Scientific Abstracts End-to-End (2023)

We consider the end-to-end abstract-to-title generation problem, exploring seven recent transformer based models (including ChatGPT) fine-tuned on more than 30k abstract-title pairs from NLP and machine learning (ML) venues. As an extension, we also consider the harder problem of generating humorous paper titles. For the latter, we compile the first large-scale humor annotated dataset for scientific papers in the NLP/ML domains, comprising 2.6k titles. We evaluate all models using human and automatic metrics. Our human evaluation suggests that our best end-to-end system per-forms similarly to human authors (but arguably slightly worse). Generating funny titles is more difficult, however, and our automatic systems clearly underperform relative to humans and often learn dataset artefacts of humor. Finally, ChatGPT, without any fine-tuning, performs on the level of our best fine-tuned system.
Summary Cycles: Exploring the Impact of Prompt Engineering on Large Language Models' Interaction with Interaction Log Information (2023)

With the aim of improving work efficiency, we examine how Large Language Models (LLMs) can better support the handoff of information by summarizing user interactions in collaborative intelligence analysis communication. We experiment with interaction logs, or a record of user interactions with a system. Inspired by chain-of-thought prompting, we describe a technique to avoid API token limits with recursive summarization requests. We then apply ChatGPT over multiple iterations to extract named entities, topics, and summaries, combined with interaction sequence sentences, to generate summaries of critical events and results of analysis sessions. We quantitatively evaluate the generated summaries against human-generated ones using common accuracy metrics (e.g., ROUGE-L, BLEU, BLEURT, and TER). We also report qualitative trends and the factuality of the output. We find that manipulating the audience feature or providing single-shot examples minimally influences the model`s accuracy. While our methodology successfully summarizes interaction logs, the lack of significant results raises questions about prompt engineering and summarization effectiveness generally. We call on explainable artificial intelligence research to better understand how terms and their placement may change LLM outputs, striving for more consistent prompt engineering guidelines.
Large Language Models As Annotators: A Preliminary Evaluation For Annotating Low-Resource Language Content (2023)

The process of collecting human-generated annotations is time-consuming and resource-hungry. In the case of low-resource (LR) languages such as Indic languages, these efforts are more expensive due to the dearth of data and human experts. Considering their importance in solving downstream applications, there have been concentrated efforts exploring alternatives for human-generated annotations. To that extent, we seek to evaluate multilingual large language models (LLMs) for their potential to substitute or aid human-generated annotation efforts. We use LLMs to re-label publicly available datasets in LR languages for the tasks of natural language inference, sentiment analysis, and news classification. We compare these annotations with existing ground truth labels to analyze the efficacy of using LLMs for annotation tasks. We observe that the performance of these LLMs varies substantially across different tasks and languages. The results show that off-the-shelf use of multilingual LLMs is not appropriate and results in poor performance in two of the three tasks.