ymoslem/acl-abstracts huggingface dataset:
You reap what you sow: On the Challenges of Bias Evaluation Under Multilingual Settings (2022)

Evaluating bias, fairness, and social impact in monolingual language models is a difficult task. This challenge is further compounded when language modeling occurs in a multilingual context. Considering the implication of evaluation biases for large multilingual language models, we situate the discussion of bias evaluation within a wider context of social scientific research with computational work. We highlight three dimensions of developing multilingual bias evaluation frameworks: (1) increasing transparency through documentation, (2) expanding targets of bias beyond gender, and (3) addressing cultural differences that exist between languages. We further discuss the power dynamics and consequences of training large language models and recommend that researchers remain cognizant of the ramifications of developing such technologies.
Diverse Lottery Tickets Boost Ensemble from a Single Pretrained Model (2022)

Ensembling is a popular method used to improve performance as a last resort. However, ensembling multiple models finetuned from a single pretrained model has been not very effective; this could be due to the lack of diversity among ensemble members. This paper proposes Multi-Ticket Ensemble, which finetunes different subnetworks of a single pretrained model and ensembles them. We empirically demonstrated that winning-ticket subnetworks produced more diverse predictions than dense networks and their ensemble outperformed the standard ensemble in some tasks when accurate lottery tickets are found on the tasks.
UNIREX: A Unified Learning Framework for Language Model Rationale Extraction (2022)

An extractive rationale explains a language model`s (LM`s) prediction on a given task instance by highlighting the text inputs that most influenced the prediction. Ideally, rationale extraction should be faithful (reflective of LM`s actual behavior) and plausible (convincing to humans), without compromising the LM`s (i.e., task model`s) task performance. Although attribution algorithms and select-predict pipelines are commonly used in rationale extraction, they both rely on certain heuristics that hinder them from satisfying all three desiderata. In light of this, we propose UNIREX, a flexible learning framework which generalizes rationale extractor optimization as follows: (1) specify architecture for a learned rationale extractor; (2) select explainability objectives (i.e., faithfulness and plausibility criteria); and (3) jointly the train task model and rationale extractor on the task using selected objectives. UNIREX enables replacing prior works' heuristic design choices with a generic learned rationale extractor in (1) and optimizing it for all three desiderata in (2)-(3). To facilitate comparison between methods w.r.t. multiple desiderata, we introduce the Normalized Relative Gain (NRG) metric. Across five English text classification datasets, our best UNIREX configuration outperforms the strongest baselines by an average of 32.9{\%} NRG. Plus, we find that UNIREX-trained rationale extractors' faithfulness can even generalize to unseen datasets and tasks.
Pipelines for Social Bias Testing of Large Language Models (2022)

The maturity level of language models is now at a stage in which many companies rely on them to solve various tasks. However, while research has shown how biased and harmful these models are, systematic ways of integrating social bias tests into development pipelines are still lacking. This short paper suggests how to use these verification techniques in development pipelines. We take inspiration from software testing and suggest addressing social bias evaluation as software testing. We hope to open a discussion on the best methodologies to handle social bias testing in language models.
Entities, Dates, and Languages: Zero-Shot on Historical Texts with T0 (2022)

In this work, we explore whether the recently demonstrated zero-shot abilities of the T0 model extend to Named Entity Recognition for out-of-distribution languages and time periods. Using a historical newspaper corpus in 3 languages as test-bed, we use prompts to extract possible named entities. Our results show that a naive approach for prompt-based zero-shot multilingual Named Entity Recognition is error-prone, but highlights the potential of such an approach for historical languages lacking labeled datasets. Moreover, we also find that T0-like models can be probed to predict the publication date and language of a document, which could be very relevant for the study of historical texts.
A Holistic Assessment of the Carbon Footprint of Noor, a Very Large Arabic Language Model (2022)

As ever larger language models grow more ubiquitous, it is crucial to consider their environmental impact. Characterised by extreme size and resource use, recent generations of models have been criticised for their voracious appetite for compute, and thus significant carbon footprint. Although reporting of carbon impact has grown more common in machine learning papers, this reporting is usually limited to compute resources used strictly for training. In this work, we propose a holistic assessment of the footprint of an extreme-scale language model, Noor. Noor is an ongoing project aiming to develop the largest multi-task Arabic language models{--}with up to 13B parameters{--}leveraging zero-shot generalisation to enable a wide range of downstream tasks via natural language instructions. We assess the total carbon bill of the entire project: starting with data collection and storage costs, including research and development budgets, pretraining costs, future serving estimates, and other exogenous costs necessary for this international cooperation. Notably, we find that inference costs and exogenous factors can have a significant impact on total budget. Finally, we discuss pathways to reduce the carbon footprint of extreme-scale models.
GPT-NeoX-20B: An Open-Source Autoregressive Language Model (2022)

We introduce GPT-NeoX-20B, a 20 billion parameter autoregressive language model trained on the Pile, whose weights will be made freely and openly available to the public through a permissive license. It is, to the best of our knowledge, the largest dense autoregressive model that has publicly available weights at the time of submission. In this work, we describe GPT-NeoX-20B`s architecture and training, and evaluate its performance. We open-source the training and evaluation code, as well as the model weights, at \url{https://github.com/EleutherAI/gpt-neox}.
Dataset Debt in Biomedical Language Modeling (2022)

Large-scale language modeling and natural language prompting have demonstrated exciting capabilities for few and zero shot learning in NLP. However, translating these successes to specialized domains such as biomedicine remains challenging, due in part to biomedical NLP`s significant dataset debt {--} the technical costs associated with data that are not consistently documented or easily incorporated into popular machine learning frameworks at scale. To assess this debt, we crowdsourced curation of datasheets for 167 biomedical datasets. We find that only 13{\%} of datasets are available via programmatic access and 30{\%} lack any documentation on licensing and permitted reuse. Our dataset catalog is available at: \url{https://tinyurl.com/bigbio22}.
Emergent Structures and Training Dynamics in Large Language Models (2022)

Large language models have achieved success on a number of downstream tasks, particularly in a few and zero-shot manner. As a consequence, researchers have been investigating both the kind of information these networks learn and how such information can be encoded in the parameters of the model. We survey the literature on changes in the network during training, drawing from work outside of NLP when necessary, and on learned representations of linguistic features in large language models. We note in particular the lack of sufficient research on the emergence of functional units, subsections of the network where related functions are grouped or organised, within large language models and motivate future work that grounds the study of language models in an analysis of their changing internal structure during training time.
Foundation Models of Scientific Knowledge for Chemistry: Opportunities, Challenges and Lessons Learned (2022)

Foundation models pre-trained on large corpora demonstrate significant gains across many natural language processing tasks and domains e.g., law, healthcare, education, etc. However, only limited efforts have investigated the opportunities and limitations of applying these powerful models to science and security applications. In this work, we develop foundation models of scientific knowledge for chemistry to augment scientists with the advanced ability to perceive and reason at scale previously unimagined. Specifically, we build large-scale (1.47B parameter) general-purpose models for chemistry that can be effectively used to perform a wide range of in-domain and out-of-domain tasks. Evaluating these models in a zero-shot setting, we analyze the effect of model and data scaling, knowledge depth, and temporality on model performance in context of model training efficiency. Our novel findings demonstrate that (1) model size significantly contributes to the task performance when evaluated in a zero-shot setting; (2) data quality (aka diversity) affects model performance more than data quantity; (3) similarly, unlike previous work, temporal order of the documents in the corpus boosts model performance only for specific tasks, e.g., SciQ; and (4) models pre-trained from scratch perform better on in-domain tasks than those tuned from general-purpose models like Open AI`s GPT-2.
Using Item Response Theory to Measure Gender and Racial Bias of a BERT-based Automated English Speech Assessment System (2022)

Recent advances in natural language processing and transformer-based models have made it easier to implement accurate, automated English speech assessments. Yet, without careful examination, applications of these models may exacerbate social prejudices based on gender and race. This study addresses the need to examine potential biases of transformer-based models in the context of automated English speech assessment. For this purpose, we developed a BERT-based automated speech assessment system and investigated gender and racial bias of examinees' automated scores. Gender and racial bias was measured by examining differential item functioning (DIF) using an item response theory framework. Preliminary results, which focused on a single verbal-response item, showed no statistically significant DIF based on gender or race for automated scores.
Automatic scoring of short answers using justification cues estimated by BERT (2022)

Automated scoring technology for short-answer questions has been attracting attention to improve the fairness of scoring and reduce the burden on the scorer. In general, a large amount of data is required to train an automated scoring model. The training data consists of the answer texts and the scoring data assigned to them. It may also include annotations indicating key word sequences. These data must be prepared manually, which is costly. Many previous studies have created models with large amounts of training data specific to each question. This paper aims to achieve equivalent performance with less training data by utilizing a BERT model that has been pre-trained on a large amount of general text data not necessarily related to short answer questions. On the RIKEN dataset, the proposed method reduces the training data from the 800 data required in the past to about 400 data, and still achieves scoring accuracy comparable to that of humans.
Mitigating Learnerese Effects for CEFR Classification (2022)

The role of an author`s L1 in SLA can be challenging for automated CEFR classification, in that texts from different L1 groups may be too heterogeneous to combine them as training data. We experiment with recent debiasing approaches by attempting to devoid textual representations of L1 features. This results in a more homogeneous group when aggregating CEFR-annotated texts from different L1 groups, leading to better classification performance. Using iterative null-space projection, we marginally improve classification performance for a linear classifier by 1 point. An MLP (e.g. non-linear) classifier remains unaffected by this procedure. We discuss possible directions of future work to attempt to increase this performance gain.
Automatically Detecting Reduced-formed English Pronunciations by Using Deep Learning (2022)

Reduced form pronunciations are widely used by native English speakers, especially in casual conversations. Second language (L2) learners have difficulty in processing reduced form pronunciations in listening comprehension and face challenges in production too. Meanwhile, training applications dedicated to reduced forms are still few. To solve this issue, we report on our first effort of using deep learning to evaluate L2 learners' reduced form pronunciations. Compared with a baseline solution that uses an ASR to determine regular or reduced-formed pronunciations, a classifier that learns representative features via a convolution neural network (CNN) on low-level acoustic features, yields higher detection performance. F-1 metric has been increased from 0.690 to 0.757 on the reduction task. Furthermore, adding word entities to compute attention weights to better adjust the features learned by the CNN model helps increasing F-1 to 0.763.
A Baseline Readability Model for Cebuano (2022)

In this study, we developed the first baseline readability model for the Cebuano language. Cebuano is the second most-used native language in the Philippines with about 27.5 million speakers. As the baseline, we extracted traditional or surface-based features, syllable patterns based from Cebuano`s documented orthography, and neural embeddings from the multilingual BERT model. Results show that the use of the first two handcrafted linguistic features obtained the best performance trained on an optimized Random Forest model with approximately 87{\%} across all metrics. The feature sets and algorithm used also is similar to previous results in readability assessment for the Filipino language{---}showing potential of crosslingual application. To encourage more work for readability assessment in Philippine languages such as Cebuano, we open-sourced both code and data.
Generation of Synthetic Error Data of Verb Order Errors for Swedish (2022)

We report on our work-in-progress to generate a synthetic error dataset for Swedish by replicating errors observed in the authentic error annotated dataset. We analyze a small subset of authentic errors, capture regular patterns based on parts of speech, and design a set of rules to corrupt new data. We explore the approach and identify its capabilities, advantages and limitations as a way to enrich the existing collection of error-annotated data. This work focuses on word order errors, specifically those involving the placement of finite verbs in a sentence.
A Dependency Treebank of Spoken Second Language English (2022)

In this paper, we introduce a dependency treebank of spoken second language (L2) English that is annotated with part of speech (Penn POS) tags and syntactic dependencies (Universal Dependencies). We then evaluate the degree to which the use of this treebank as training data affects POS and UD annotation accuracy for L1 web texts, L2 written texts, and L2 spoken texts as compared to models trained on L1 texts only.
Starting from \textquotedblleftZero\textquotedblright: An Incremental Zero-shot Learning Approach for Assessing Peer Feedback Comments (2022)

Peer assessment is an effective and efficient pedagogical strategy for delivering feedback to learners. Asking students to provide quality feedback, which contains suggestions and mentions problems, can promote metacognition by reviewers and better assist reviewees in revising their work. Thus, various supervised machine learning algorithms have been proposed to detect quality feedback. However, all these powerful algorithms have the same Achilles' heel: the reliance on sufficient historical data. In other words, collecting adequate peer feedback for training a supervised algorithm can take several semesters before the model can be deployed to a new class. In this paper, we present a new paradigm, called incremental zero-shot learning (IZSL), to tackle the problem of lacking sufficient historical data. Our results show that the method can achieve acceptable {\textquotedblleft}cold-start{\textquotedblright} performance without needing any domain data, and it outperforms BERT when trained on the same data collected incrementally.
On Assessing and Developing Spoken `Grammatical Error Correction' Systems (2022)

Spoken {\textquoteleft}grammatical error correction' (SGEC) is an important process to provide feedback for second language learning. Due to a lack of end-to-end training data, SGEC is often implemented as a cascaded, modular system, consisting of speech recognition, disfluency removal, and grammatical error correction (GEC). This cascaded structure enables efficient use of training data for each module. It is, however, difficult to compare and evaluate the performance of individual modules as preceeding modules may introduce errors. For example the GEC module input depends on the output of non-native speech recognition and disfluency detection, both challenging tasks for learner data. This paper focuses on the assessment and development of SGEC systems. We first discuss metrics for evaluating SGEC, both individual modules and the overall system. The system-level metrics enable tuning for optimal system performance. A known issue in cascaded systems is error propagation between modules. To mitigate this problem semi-supervised approaches and self-distillation are investigated. Lastly, when SGEC system gets deployed it is important to give accurate feedback to users. Thus, we apply filtering to remove edits with low-confidence, aiming to improve overall feedback precision. The performance metrics are examined on a Linguaskill multi-level data set, which includes the original non-native speech, manual transcriptions and reference grammatical error corrections, to enable system analysis and development.
Automatic True/False Question Generation for Educational Purpose (2022)

In field of teaching, true/false questioning is an important educational method for assessing students' general understanding of learning materials. Manually creating such questions requires extensive human effort and expert knowledge. Question Generation (QG) technique offers the possibility to automatically generate a large number of questions. However, there is limited work on automatic true/false question generation due to the lack of training data and difficulty finding question-worthy content. In this paper, we propose an unsupervised True/False Question Generation approach (TF-QG) that automatically generates true/false questions from a given passage for reading comprehension test. TF-QG consists of a template-based framework that aims to test the specific knowledge in the passage by leveraging various NLP techniques, and a generative framework to generate more flexible and complicated questions by using a novel masking-and-infilling strategy. Human evaluation shows that our approach can generate high-quality and valuable true/false questions. In addition, simulated testing on the generated questions challenges the state-of-the-art inference models from NLI, QA, and fact verification tasks.
Fine-tuning Transformers with Additional Context to Classify Discursive Moves in Mathematics Classrooms (2022)

{\textquotedblleft}Talk moves{\textquotedblright} are specific discursive strategies used by teachers and students to facilitate conversations in which students share their thinking, and actively consider the ideas of others, and engage in rich discussions. Experts in instructional practices often rely on cues to identify and document these strategies, for example by annotating classroom transcripts. Prior efforts to develop automated systems to classify teacher talk moves using transformers achieved a performance of 76.32{\%} F1. In this paper, we investigate the feasibility of using enriched contextual cues to improve model performance. We applied state-of-the-art deep learning approaches for Natural Language Processing (NLP), including Robustly optimized bidirectional encoder representations from transformers (Roberta) with a special input representation that supports previous and subsequent utterances as context for talk moves classification. We worked with the publically available TalkMoves dataset, which contains utterances sourced from real-world classroom sessions (human- transcribed and annotated). Through a series of experimentations, we found that a combination of previous and subsequent utterances improved the transformers' ability to differentiate talk moves (by 2.6{\%} F1). These results constitute a new state of the art over previously published results and provide actionable insights to those in the broader NLP community who are working to develop similar transformer-based classification models.
Cross-corpora experiments of automatic proficiency assessment and error detection for spoken English (2022)

The growing demand for learning English as a second language has led to an increasing interest in automatic approaches for assessing spoken language proficiency. One of the most significant challenges in this field is the lack of publicly available annotated spoken data. Another common issue is the lack of consistency and coherence in human assessment. To tackle both problems, in this paper we address the task of automatically predicting the scores of spoken test responses of English-as-a-second-language learners by training neural models on written data and using the presence of grammatical errors as a feature, as they can be considered consistent indicators of proficiency through their distribution and frequency. Specifically, we train a feature extractor on EFCAMDAT, a large written corpus containing error annotations and proficiency levels assigned by human experts, in order to extract information related to grammatical errors and, in turn, we use the resulting model for inference on the CLC-FCE corpus, on the ICNALE corpus, and on the spoken section of the TLT-school corpus, a collection of proficiency tests taken by Italian students. The work investigates the impact of the feature extractor on spoken proficiency assessment as well as the written-to-spoken approach. We find that our error-based approach can be beneficial for assessing spoken proficiency. The results obtained on the considered datasets are discussed and evaluated with appropriate metrics.