Language models strongly rely on frequency information because they maximize the likelihood of tokens during pre-training. As a consequence, language models tend to not generalize well to tokens that are seldom seen during training. Moreover, maximum likelihood training has been discovered to give rise to anisotropy: representations of tokens in a model tend to cluster tightly in a high-dimensional cone, rather than spreading out over their representational capacity.Our work introduces a method for quantifying the frequency bias of a language model by assessing sentence-level perplexity with respect to token-level frequency. We then present a method for reducing the frequency bias of a language model by inducing a syntactic prior over token representations during pre-training. Our Syntactic Smoothing method adjusts the maximum likelihood objective function to distribute the learning signal to syntactically similar tokens. This approach results in better performance on infrequent English tokens and a decrease in anisotropy. We empirically show that the degree of anisotropy in a model correlates with its frequency bias.

We present a novel approach to extracting recurring narrative patterns, or type-scenes, in Biblical Hebrew and Biblical Greek with an information retrieval network. We use cross-references to train an encoder model to create similar representations for verses linked by a cross-reference. We then query our trained model with phrases informed by humanities scholarship and designed to elicit particular kinds of narrative scenes. Our models can surface relevant instances in the top-10 ranked candidates in many cases.Through manual error analysis and discussion, we address the limitations and challenges inherent in our approach. Our findings contribute to the field of Biblical scholarship by offering a new perspective on narrative analysis within ancient texts, and to computational modeling of narrative with a genre-agnostic approach for pattern-finding in long, literary texts.

Thanks to recent advances in generative AI, we are able to prompt large language models (LLMs) to produce texts which are fluent and grammatical. In addition, it has been shown that we can elicit attempts at grammatical error correction (GEC) from LLMs when prompted with ungrammatical input sentences. We evaluate how well LLMs can perform at GEC by measuring their performance on established benchmark datasets. We go beyond previous studies, which only examined GPT* models on a selection of English GEC datasets, by evaluating seven open-source and three commercial LLMs on four established GEC benchmarks. We investigate model performance and report results against individual error types. Our results indicate that LLMs do not always outperform supervised English GEC models except in specific contexts – namely commercial LLMs on benchmarks annotated with fluency corrections as opposed to minimal edits. We find that several open-source models outperform commercial ones on minimal edit benchmarks, and that in some settings zero-shot prompting is just as competitive as few-shot prompting.

Code-switching (CSW) is a common phenomenon among multilingual speakers where multiple languages are used in a single discourse or utterance. Mixed language utterances may still contain grammatical errors however, yet most existing Grammar Error Correction (GEC) systems have been trained on monolingual data and not developed with CSW in mind. In this work, we conduct the first exploration into the use of GEC systems on CSW text. Through this exploration, we propose a novel method of generating synthetic CSW GEC datasets by translating different spans of text within existing GEC corpora. We then investigate different methods of selecting these spans based on CSW ratio, switch-point factor and linguistic constraints, and identify how they affect the performance of GEC systems on CSW text. Our best model achieves an average increase of 1.57 F0.5 across 3 CSW test sets (English-Chinese, English-Korean and English-Japanese) without affecting the model’s performance on a monolingual dataset. We furthermore discovered that models trained on one CSW language generalise relatively well to other typologically similar CSW languages.

Essay writing is a skill commonly taught and practised in schools. The ability to write a fluent and persuasive essay is often a major component of formal assessment. In natural language processing and education technology we may work with essays in their final form, for example to carry out automated assessment or grammatical error correction. In this work we collect and analyse data representing the essay writing process from start to finish, by recording every key stroke from multiple writers participating in our study. We describe our data collection methodology, the characteristics of the resulting dataset, and the assignment of proficiency levels to the texts. We discuss the ways the keystroke data can be used – for instance seeking to identify patterns in the keystrokes which might act as features in automated assessment or may enable further advancements in writing assistance – and the writing support technology which could be built with such information, if we can detect when writers are struggling to compose a section of their essay and offer appropriate intervention. We frame this work in the context of English language learning, but we note that keystroke logging is relevant more broadly to text authoring scenarios as well as cognitive or linguistic analyses of the writing process.

Targeted studies testing knowledge of subject-verb agreement (SVA) indicate that pre-trained language models encode syntactic information. We assert that if models robustly encode subject-verb agreement, they should be able to identify when agreement is correct and when it is incorrect. To that end, we propose grammatical error detection as a diagnostic probe to evaluate token-level contextual representations for their knowledge of SVA. We evaluate contextual representations at each layer from five pre-trained English language models: BERT, XLNet, GPT-2, RoBERTa and ELECTRA. We leverage public annotated training data from both English second language learners and Wikipedia edits, and report results on manually crafted stimuli for subject-verb agreement. We find that masked language models linearly encode information relevant to the detection of SVA errors, while the autoregressive models perform on par with our baseline. However, we also observe a divergence in performance when probes are trained on different training sets, and when they are evaluated on different syntactic constructions, suggesting the information pertaining to SVA error detection is not robustly encoded.

The detection of mental health conditions based on an individual’s use of language has received considerable attention in the NLP community. However, most work has focused on single-task and single-domain models, limiting the semantic space that they are able to cover and risking significant cross-domain loss. In this paper, we present two approaches towards a unified framework for cross-domain and cross-task learning for the detection of depression, post-traumatic stress disorder and suicide risk across different platforms that further utilizes inductive biases across tasks. Firstly, we develop a lightweight model using a general set of features that sets a new state of the art on several tasks while matching the performance of more complex task- and domain-specific systems on others. We also propose a multi-task approach and further extend our framework to explicitly capture the affective characteristics of someone’s language, further consolidating transfer of inductive biases and of shared linguistic characteristics. Finally, we present a novel dynamically adaptive loss weighting approach that allows for more stable learning across imbalanced datasets and better neural generalization performance. Our results demonstrate the effectiveness of our unified framework for mental ill-health detection across a number of diverse English datasets.

With the growth of online learning through MOOCs and other educational applications, it has become increasingly difficult for course providers to offer personalized feedback to students. Therefore asking students to provide feedback to each other has become one way to support learning. This peer-to-peer feedback has become increasingly important whether in MOOCs to provide feedback to thousands of students or in large-scale classes at universities. One of the challenges when allowing peer-to-peer feedback is that the feedback should be perceived as helpful, and an import factor determining helpfulness is how specific the feedback is. However, in classes including thousands of students, instructors do not have the resources to check the specificity of every piece of feedback between students. Therefore, we present an automatic classification model to measure sentence specificity in written feedback. The model was trained and tested on student feedback texts written in German where sentences have been labelled as general or specific. We find that we can automatically classify the sentences with an accuracy of 76.7% using a conventional feature-based approach, whereas transfer learning with BERT for German gives a classification accuracy of 81.1%. However, the feature-based approach comes with lower computational costs and preserves human interpretability of the coefficients. In addition we show that specificity of sentences in feedback texts has a weak positive correlation with perceptions of helpfulness. This indicates that specificity is one of the ingredients of good feedback, and invites further investigation.

This paper introduces a novel tool to support and engage English language learners with feedback on the quality of their argument structures. We present an approach which automatically detects claim-premise structures and provides visual feedback to the learner to prompt them to repair any broken argumentation structures. To investigate, if our persuasive feedback on language learners’ essay writing tasks engages and supports them in learning better English language, we designed the ALEN app (Argumentation for Learning English). We leverage an argumentation mining model trained on texts written by students and embed it in a writing support tool which provides students with feedback in their essay writing process. We evaluated our tool in two field-studies with a total of 28 students from a German high school to investigate the effects of adaptive argumentation feedback on their learning of English. The quantitative results suggest that using the ALEN app leads to a high self-efficacy, ease-of-use, intention to use and perceived usefulness for students in their English language learning process. Moreover, the qualitative answers indicate the potential benefits of combining grammar feedback with discourse level argumentation mining.

State-of-the-art chatbots for English are now able to hold conversations on virtually any topic (e.g. Adiwardana et al., 2020; Roller et al., 2021). However, existing dialogue systems in the language learning domain still use hand-crafted rules and pattern matching, and are much more limited in scope. In this paper, we make an initial foray into adapting open-domain dialogue generation for second language learning. We propose and implement decoding strategies that can adjust the difficulty level of the chatbot according to the learner’s needs, without requiring further training of the chatbot. These strategies are then evaluated using judgements from human examiners trained in language education. Our results show that re-ranking candidate outputs is a particularly effective strategy, and performance can be further improved by adding sub-token penalties and filtering.

The most successful approach to Neural Machine Translation (NMT) when only monolingual training data is available, called unsupervised machine translation, is based on back-translation where noisy translations are generated to turn the task into a supervised one. However, back-translation is computationally very expensive and inefficient. This work explores a novel, efficient approach to unsupervised NMT. A transformer, initialized with cross-lingual language model weights, is fine-tuned exclusively on monolingual data of the target language by jointly learning on a paraphrasing and denoising autoencoder objective. Experiments are conducted on WMT datasets for German-English, French-English, and Romanian-English. Results are competitive to strong baseline unsupervised NMT models, especially for closely related source languages (German) compared to more distant ones (Romanian, French), while requiring about a magnitude less training time.

We describe the collection of transcription corrections and grammatical error annotations for the CrowdED Corpus of spoken English monologues on business topics. The corpus recordings were crowdsourced from native speakers of English and learners of English with German as their first language. The new transcriptions and annotations are obtained from different crowdworkers: we analyse the 1108 new crowdworker submissions and propose that they can be used for automatic transcription post-editing and grammatical error correction for speech. To further explore the data we train grammatical error detection models with various configurations including pre-trained and contextual word representations as input, additional features and auxiliary objectives, and extra training data from written error-annotated corpora. We find that a model concatenating pre-trained and contextual word representations as input performs best, and that additional information does not lead to further performance gains.

We present a lightweight method for identifying currently trending terms in relation to a known prior of terms, using a weighted log-odds ratio with an informative prior. We apply this method to a dataset of posts from an English-language underground hacking forum, spanning over ten years of activity, with posts containing misspellings, orthographic variation, acronyms, and slang. Our statistical approach supports analysis of linguistic change and discussion topics over time, without a requirement to train a topic model for each time interval for analysis. We evaluate the approach by comparing the results to TF-IDF using the discounted cumulative gain metric with human annotations, finding our method outperforms TF-IDF on information retrieval.

We report on our attempts to reproduce the work described in Vajjala & Rama 2018, ‘Experiments with universal CEFR classification’, as part of REPROLANG 2020: this involves featured-based and neural approaches to essay scoring in Czech, German and Italian. Our results are broadly in line with those from the original paper, with some differences due to the stochastic nature of machine learning and programming language used. We correct an error in the reported metrics, introduce new baselines, apply the experiments to English and Spanish corpora, and generate adversarial data to test classifier robustness. We conclude that feature-based approaches perform better than neural network classifiers for text datasets of this size, though neural network modifications do bring performance closer to the best feature-based models.

We address the task of automatically grading the language proficiency of spontaneous speech based on textual features from automatic speech recognition transcripts. Motivated by recent advances in multi-task learning, we develop neural networks trained in a multi-task fashion that learn to predict the proficiency level of non-native English speakers by taking advantage of inductive transfer between the main task (grading) and auxiliary prediction tasks: morpho-syntactic labeling, language modeling, and native language identification (L1). We encode the transcriptions with both bi-directional recurrent neural networks and with bi-directional representations from transformers, compare against a feature-rich baseline, and analyse performance at different proficiency levels and with transcriptions of varying error rates. Our best performance comes from a transformer encoder with L1 prediction as an auxiliary task. We discuss areas for improvement and potential applications for text-only speech scoring.

In historical linguistics, cognate detection is the task of determining whether sets of words have common etymological roots. Inspired by the comparative method used by human linguists, we develop a system for automated cognate detection that frames the task as an inference problem for a general statistical model consisting of observed data (potentially cognate pairs of words), latent variables (the cognacy status of pairs) and unknown global parameters (which sounds correspond between languages). We then give a specific instance of such a model along with an expectation-maximisation algorithm to infer its parameters. We evaluate our system on a corpus of 8140 cognate sets, finding the performance of our method to be comparable to the state of the art. We additionally carry out qualitative analysis demonstrating advantages it has over existing systems. We also suggest several ways our work could be extended within the general theoretical framework we propose.

We describe the CAMsterdam team entry to the SemEval-2019 Shared Task 6 on offensive language identification in Twitter data. Our proposed model learns to extract textual features using a multi-layer recurrent network, and then performs text classification using gradient-boosted decision trees (GBDT). A self-attention architecture enables the model to focus on the most relevant areas in the text. In order to enrich input representations, we use node2vec to learn globally optimised embeddings for hashtags, which are then given as additional features to the GBDT classifier. Our best model obtains 78.79% macro F1-score on detecting offensive language (subtask A), 66.32% on categorising offence types (targeted/untargeted; subtask B), and 55.36% on identifying the target of offence (subtask C).

We probe the heterogeneity in levels of abusive language in different sections of the Internet, using an annotated corpus of Wikipedia page edit comments to train a binary classifier for abuse detection. Our test data come from the CrimeBB Corpus of hacking-related forum posts and we find that (a) forum interactions are rarely abusive, (b) the abusive language which does exist tends to be relatively mild compared to that found in the Wikipedia comments domain, and tends to involve aggressive posturing rather than hate speech or threats of violence. We observe that the purpose of conversations in online forums tend to be more constructive and informative than those in Wikipedia page edit comments which are geared more towards adversarial interactions, and that this may explain the lower levels of abuse found in our forum data than in Wikipedia comments. Further work remains to be done to compare these results with other inter-domain classification experiments, and to understand the impact of aggressive language in forum conversations.

This paper investigates the problem of text normalisation; specifically, the normalisation of non-standard words (NSWs) in English. Non-standard words can be defined as those word tokens which do not have a dictionary entry, and cannot be pronounced using the usual letter-to-phoneme conversion rules; e.g. lbs, 99.3%, #EMNLP2017. NSWs pose a challenge to the proper functioning of text-to-speech technology, and the solution is to spell them out in such a way that they can be pronounced appropriately. We describe our four-stage normalisation system made up of components for detection, classification, division and expansion of NSWs. Performance is favourabe compared to previous work in the field (Sproat et al. 2001, Normalization of non-standard words), as well as state-of-the-art text-to-speech software. Further, we update Sproat et al.’s NSW taxonomy, and create a more customisable system where users are able to input their own abbreviations and specify into which variety of English (currently available: British or American) they wish to normalise.

We present an analysis of parser performance on speech data, comparing word type and token frequency distributions with written data, and evaluating parse accuracy by length of input string. We find that parser performance tends to deteriorate with increasing length of string, more so for spoken than for written texts. We train an alternative parsing model with added speech data and demonstrate improvements in accuracy on speech-units, with no deterioration in performance on written text.

We present crowdsourced collection of error annotations for transcriptions of spoken learner English. Our emphasis in data collection is on fluency corrections, a more complete correction than has traditionally been aimed for in grammatical error correction research (GEC). Fluency corrections require improvements to the text, taking discourse and utterance level semantics into account: the result is a more naturalistic, holistic version of the original. We propose that this shifted emphasis be reflected in a new name for the task: ‘holistic error correction’ (HEC). We analyse crowdworker behaviour in HEC and conclude that the method is useful with certain amendments for future work.

We announce the release of the CROWDED CORPUS: a pair of speech corpora collected via crowdsourcing, containing a native speaker corpus of English (CROWDED_ENGLISH), and a corpus of German/English bilinguals (CROWDED_BILINGUAL). Release 1 of the CROWDED CORPUS contains 1000 recordings amounting to 33,400 tokens collected from 80 speakers and is freely available to other researchers. We recruited participants via the Crowdee application for Android. Recruits were prompted to respond to business-topic questions of the type found in language learning oral tests. We then used the CrowdFlower web application to pass these recordings to crowdworkers for transcription and annotation of errors and sentence boundaries. Finally, the sentences were tagged and parsed using standard natural language processing tools. We propose that crowdsourcing is a valid and economical method for corpus collection, and discuss the advantages and disadvantages of this approach.

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In order to apply computational linguistic analyses and pass information to downstream applications, transcriptions of speech obtained via automatic speech recognition (ASR) need to be divided into smaller meaningful units, in a task we refer to as ‘speech-unit (SU) delimitation’. We closely recreate the automatic delimitation system described by Lee and Glass (2012), ‘Sentence detection using multiple annotations’, Proceedings of INTERSPEECH, which combines a prosodic model, language model and speech-unit length model in log-linear fashion. Since state-of-the-art natural language processing (NLP) tools have been developed to deal with written text and its characteristic sentence-like units, SU delimitation helps bridge the gap between ASR and NLP, by normalising spoken data into a more canonical format. Previous work has focused on native speaker recordings; we test the system of Lee and Glass (2012) on non-native speaker (or ‘learner’) data, achieving performance above the state-of-the-art. We also consider alternative evaluation metrics which move away from the idea of a single ‘truth’ in SU delimitation, and frame this work in the context of downstream NLP applications.

Researchers in the fields of psycholinguistics and neurolinguistics increasingly test their experimental hypotheses against probabilistic models of language. VALEX (Korhonen et al., 2006) is a large-scale verb lexicon that specifies verb usage as probability distributions over a set of 163 verb SUBCATEGORIZATION FRAMES (SCFs). VALEX has proved to be a popular computational linguistic resource and may also be used by psycho- and neurolinguists for experimental analysis and stimulus generation. However, a probabilistic model based upon a set of 163 SCFs often proves too fine grained for experimenters in these fields. Our goal is to simplify the classification by grouping the frames into genera―explainable clusters that may be used as experimental parameters. We adopted two methods for reclassification. One was a manual linguistic approach derived from verb argumentation and clause features; the other was an automatic, computational approach driven from a graphical representation of SCFs. The premise was not only to compare the results of two quite different methods for our own interest, but also to enable other researchers to choose whichever reclassification better suited their purpose (one being grounded purely in theoretical linguistics and the other in practical language engineering). The various classifications are available as an online resource to researchers.

We present a set of stand-off annotations for the ninety thousand sentences in the spoken section of the British National Corpus (BNC) which feature a progressive aspect verb group. These annotations may be matched to the original BNC text using the supplied document and sentence identifiers. The annotated features mostly relate to linguistic form: subject type, subject person and number, form of auxiliary verb, and clause type, tense and polarity. In addition, the sentences are classified for register, the formality of recording context: three levels of `spontaneity' with genres such as sermons and scripted speech at the most formal level and casual conversation at the least formal. The resource has been designed so that it may easily be augmented with further stand-off annotations. Expert linguistic annotations of spoken data, such as these, are valuable for improving the performance of natural language processing tools in the spoken language domain and assist linguistic research in general.