Leveraging large language models (LLMs) for various natural language processing tasks has led to superlative claims about their performance. For the evaluation of machine translation (MT), existing research shows that LLMs are able to achieve results comparable to fine-tuned multilingual pre-trained language models. In this paper, we explore what translation information, such as the source, reference, translation errors and annotation guidelines, is needed for LLMs to evaluate MT quality. In addition, we investigate prompting techniques such as zero-shot, Chain of Thought (CoT) and few-shot prompting for eight language pairs covering high-, medium- and low-resource languages, leveraging varying LLM variants. Our findings indicate the importance of reference translations for an LLM-based evaluation. While larger models do not necessarily fare better, they tend to benefit more from CoT prompting, than smaller models. We also observe that LLMs do not always provide a numerical score when generating evaluations, which poses a question on their reliability for the task. Our work presents a comprehensive analysis for resource-constrained and training-less LLM-based evaluation of machine translation. We release the accrued prompt templates, code and data publicly for reproducibility.

This paper addresses the challenge of improving user experience on e-commerce platforms by enhancing product ranking relevant to user’s search queries. Ambiguity and complexity of user queries often lead to a mismatch between user’s intent and retrieved product titles or documents. Recent approaches have proposed the use of Transformer-based models which need millions of annotated query-title pairs during the pre-training stage, and this data often does not take user intent into account. To tackle this, we curate samples from existing datasets at eBay, manually annotated with buyer-centric relevance scores, and centrality scores which reflect how well the product title matches the user’s intent. We introduce a User-intent Centrality Optimization (UCO) approach for existing models, which optimizes for the user intent in semantic product search. To that end, we propose a dual-loss based optimization to handle hard negatives, i.e., product titles that are semantically relevant but do not reflect the user’s intent. Our contributions include curating challenging evaluation sets and implementing UCO, resulting in significant improvements in product ranking efficiency, observed for different evaluation metrics. Our work aims to ensure that the most buyer-centric titles for a query are ranked higher, thereby, enhancing the user experience on e-commerce platforms.

Easy-to-Understand (E2U) language varieties have been recognized by the United Nation’s Convention on the Rights of Persons with Disabilities (2006) as a means to guarantee the fundamental right to Accessible Communication. Increased awareness has driven changes in European (European Commission, 2015, 2021; European Parliament, 2016) and International legislation (ODI, 2010), prompting public-sector and other institutions to offer domain-specific content into E2U language to prevent communicative exclusion of those facing cognitive barriers (COGA, 2017; Maaß, 2020; Perego, 2020). However, guidance on what it is that makes language actually ‘easier to understand’ is still fragmented and vague. For this reason, we carried out a systematic review of official guidelines for English Plain Language and Easy Language to identify the most effective lexical, syntactic and adaptation strategies that can reduce complexity in verbal discourse according to official bodies. This article will present the methods and preliminary results of the guidelines analysis.

This paper presents a dataset for evaluating the machine translation of emotion-loaded user generated content. It contains human-annotated quality evaluation data and post-edited reference translations. The dataset is available at our GitHub repository.

We report the results of the WMT 2024 shared task on Quality Estimation, in which the challenge is to predict the quality of the output of neural machine translation systems at the word and sentence levels, without access to reference translations. In this edition, we expanded our scope to assess the potential for quality estimates to help in the correction of translated outputs, hence including an automated post-editing (APE) direction. We publish new test sets with human annotations that target two directions: providing new Multidimensional Quality Metrics (MQM) annotations for three multi-domain language pairs (English to German, Spanish and Hindi) and extending the annotations on Indic languages providing direct assessments and post edits for translation from English into Hindi, Gujarati, Tamil and Telugu. We also perform a detailed analysis of the behaviour of different models with respect to different phenomena including gender bias, idiomatic language, and numerical and entity perturbations. We received submissions based both on traditional, encoder-based approaches as well as large language model (LLM) based ones.

Machine translation (MT) of user-generated content (UGC) poses unique challenges, including handling slang, emotion, and literary devices like irony and sarcasm. Evaluating the quality of these translations is challenging as current metrics do not focus on these ubiquitous features of UGC. To address this issue, we utilize an existing emotion-related dataset that includes emotion labels and human-annotated translation errors based on Multi-dimensional Quality Metrics. We extend it with sentence-level evaluation scores and word-level labels, leading to a dataset suitable for sentence- and word-level translation evaluation and emotion classification, in a multi-task setting. We propose a new architecture to perform these tasks concurrently, with a novel combined loss function, which integrates different loss heuristics, like the Nash and Aligned losses. Our evaluation compares existing fine-tuning and multi-task learning approaches, assessing generalization with ablative experiments over multiple datasets. Our approach achieves state-of-the-art performance and we present a comprehensive analysis for MT evaluation of UGC.

This paper investigates whether large language models (LLMs) are state-of-the-art quality estimators for machine translation of user-generated content (UGC) that contains emotional expressions, without the use of reference translations. To achieve this, we employ an existing emotion-related dataset with human-annotated errors and calculate quality evaluation scores based on the Multi-dimensional Quality Metrics. We compare the accuracy of several LLMs with that of our fine-tuned baseline models, under in-context learning and parameter-efficient fine-tuning (PEFT) scenarios. We find that PEFT of LLMs leads to better performance in score prediction with human interpretable explanations than fine-tuned models. However, a manual analysis of LLM outputs reveals that they still have problems such as refusal to reply to a prompt and unstable output while evaluating machine translation of UGC.

Abbreviations and their associated long forms are important textual elements that are present in almost every scientific communication, and having information about these forms can help improve several NLP tasks. In this paper, our aim is to fine-tune language models for automatically identifying abbreviations and long forms. We used existing datasets which are annotated with abbreviations and long forms to train and test several language models, including transformer models, character-level language models, stacking of different embeddings, and ensemble methods. Our experiments showed that it was possible to achieve state-of-the-art results by stacking RoBERTa embeddings with domain-specific embeddings. However, the analysis of our first run showed that one of the datasets had issues in the BIO annotation, which led us to propose a revised dataset. After re-training selected models on the revised dataset, results show that character-level models achieve comparable results, especially when detecting abbreviations, but both RoBERTa large and the stacking of embeddings presented better results on biomedical data. When tested on a different subdomain (segments extracted from computer science texts), an ensemble method proved to yield the best results for the detection of long forms, and a character-level model had the best performance in detecting abbreviations.

One the most important archived legal material in the UK is the video recordings of Supreme Court hearings and their corresponding judgements. The impact of Supreme Court published material extends far beyond the parties involved in any given case as it provides landmark rulings on points of law of the greatest public and constitutional importance. Typically, transcripts of legal hearings are lengthy, making it time-consuming for legal professionals to analyse crucial arguments. This study focuses on summarising the second phase of a collaborative research-industrial project aimed at creating an automatic tool designed to connect sections of written judgements with relevant moments in Supreme Court hearing videos, streamlining access to critical information. Acting as a User-Interface (UI) platform, the tool enhances access to justice by pinpointing significant moments in the videos, aiding in comprehension of the final judgement. We make available the initial dataset of judgement-hearing pairs for legal Information Retrieval research, and elucidate our use of AI generative technology to enhance it. Additionally, we demonstrate how fine-tuning GPT text embeddings to our dataset optimises accuracy for an automated linking system tailored to the legal domain.

Quality Estimation (QE) is the task of evaluating machine translation output in the absence of reference translation. Conventional approaches to QE involve training separate models at different levels of granularity viz., word-level, sentence-level, and document-level, which sometimes lead to inconsistent predictions for the same input. To overcome this limitation, we focus on jointly training a single model for sentence-level and word-level QE tasks in a multi-task learning framework. Using two multi-task learning-based QE approaches, we show that multi-task learning improves the performance of both tasks. We evaluate these approaches by performing experiments in different settings, viz., single-pair, multi-pair, and zero-shot. We compare the multi-task learning-based approach with baseline QE models trained on single tasks and observe an improvement of up to 4.28% in Pearson’s correlation (r) at sentence-level and 8.46% in F1-score at word-level, in the single-pair setting. In the multi-pair setting, we observe improvements of up to 3.04% at sentence-level and 13.74% at word-level; while in the zero-shot setting, we also observe improvements of up to 5.26% and 3.05%, respectively. We make the models proposed in this paper publically available.

One the most important archived legal material in the UK is the Supreme Court published judgements and video recordings of court sittings for the decided cases. The impact of Supreme Court published material extends far beyond the parties involved in any given case as it provides landmark rulings on arguable points of law of the greatest public and constitutional importance. However, the recordings of a case are usually very long which makes it both time and effort consuming for legal professionals to study the critical arguments in the legal deliberations. In this research, we summarise the second part of a combined research-industrial project for building an automated tool designed specifically to link segments in the text judgement to semantically relevant timespans in the videos of the hearings. The tool is employed as a User-Interface (UI) platform that provides a better access to justice by bookmarking the timespans in the videos which contributed to the final judgement of the case. We explain how we employ AI generative technology to retrieve the relevant links and show that the customisation of the GPT text embeddings to our dataset achieves the best accuracy for our automatic linking system.

In this paper, we focus on how current Machine Translation (MT) engines perform on the translation of emotion-loaded texts by evaluating outputs from Google Translate according to a framework proposed in this paper. We propose this evaluation framework based on the Multidimensional Quality Metrics (MQM) and perform detailed error analyses of the MT outputs. From our analysis, we observe that about 50% of MT outputs are erroneous in preserving emotions. After further analysis of the erroneous examples, we find that emotion carrying words and linguistic phenomena such as polysemous words, negation, abbreviation etc., are common causes for these translation errors.

It is common for websites that contain User-Generated Text (UGT) to provide an automatic translation option to reach out to their linguistically diverse users. In such scenarios, the process of translating the users’ emotions is entirely automatic with no human intervention, neither for post-editing, nor for accuracy checking. In this paper, we assess whether automatic translation tools can be a successful real-life utility in transferring emotion in multilingual tweets. Our analysis shows that the mistranslation of the source tweet can lead to critical errors where the emotion is either completely lost or flipped to an opposite sentiment. We identify linguistic phenomena specific to Twitter data which pose a challenge in translation of emotions and show how frequent these features are in different language pairs. We also show that commonly-used quality metrics can lend false confidence in the performance of online MT tools specifically when the source emotion is distorted in telegraphic messages such as tweets.

This article surveys the most important ways in which translators can use ChatGPT. The focus is on scenarios where ChatGPT supports the work of translators, rather than tries to replace them. A discussion of issues that translators need to consider when using large language models, and ChatGPT in particular, is also provided.

We report the results of the WMT 2023 shared task on Quality Estimation, in which the challenge is to predict the quality of the output of neural machine translation systems at the word and sentence levels, without access to reference translations. This edition introduces a few novel aspects and extensions that aim to enable more fine-grained, and explainable quality estimation approaches. We introduce an updated quality annotation scheme using Multidimensional Quality Metrics to obtain sentence- and word-level quality scores for three language pairs. We also extend the provided data to new language pairs: we specifically target low-resource languages and provide training, development and test data for English-Hindi, English-Tamil, English-Telegu and English-Gujarati as well as a zero-shot test-set for English-Farsi. Further, we introduce a novel fine-grained error prediction task aspiring to motivate research towards more detailed quality predictions.

Quality Estimation (QE) systems are important in situations where it is necessary to assess the quality of translations, but there is no reference available. This paper describes the approach adopted by the SurreyAI team for addressing the Sentence-Level Direct Assessment shared task in WMT23. The proposed approach builds upon the TransQuest framework, exploring various autoencoder pre-trained language models within the MonoTransQuest architecture using single and ensemble settings. The autoencoder pre-trained language models employed in the proposed systems are XLMV, InfoXLM-large, and XLMR-large. The evaluation utilizes Spearman and Pearson correlation coefficients, assessing the relationship between machine-predicted quality scores and human judgments for 5 language pairs (English-Gujarati, English-Hindi, English-Marathi, English-Tamil and English-Telugu). The MonoTQ-InfoXLM-large approach emerges as a robust strategy, surpassing all other individual models proposed in this study by significantly improving over the baseline for the majority of the language pairs.

This paper attempts to identify challenges professional translators face when translating emotion-loaded texts as well as errors machine translation (MT) makes when translating this content. We invited ten Chinese-English translators to translate thirty posts of a Chinese microblog, and interviewed them about the challenges encountered during translation and the problems they believe MT might have. Further, we analysed more than five-thousand automatic translations of microblog posts to observe problems in MT outputs. We establish that the most challenging problem for human translators is emotion-carrying words, which translators also consider as a problem for MT. Analysis of MT outputs shows that this is also the most common source of MT errors. We also find that what is challenging for MT, such as non-standard writing, is not necessarily an issue for humans. Our work contributes to a better understanding of the challenges for the translation of microblog posts by humans and MT, caused by different forms of expression of emotion.

In the online world, Machine Translation (MT) systems are extensively used to translate User-Generated Text (UGT) such as reviews, tweets, and social media posts, where the main message is often the author’s positive or negative attitude towards the topic of the text. However, MT systems still lack accuracy in some low-resource languages and sometimes make critical translation errors that completely flip the sentiment polarity of the target word or phrase and hence delivers a wrong affect message. This is particularly noticeable in texts that do not follow common lexico-grammatical standards such as the dialectical Arabic (DA) used on online platforms. In this research, we aim to improve the translation of sentiment in UGT written in the dialectical versions of the Arabic language to English. Given the scarcity of gold-standard parallel data for DA-EN in the UGT domain, we introduce a semi-supervised approach that exploits both monolingual and parallel data for training an NMT system initialised by a cross-lingual language model trained with supervised and unsupervised modeling objectives. We assess the accuracy of sentiment translation by our proposed system through a numerical ‘sentiment-closeness’ measure as well as human evaluation. We will show that our semi-supervised MT system can significantly help with correcting sentiment errors detected in the online translation of dialectical Arabic UGT.

The detection and extraction of abbreviations from unstructured texts can help to improve the performance of Natural Language Processing tasks, such as machine translation and information retrieval. However, in terms of publicly available datasets, there is not enough data for training deep-neural-networks-based models to the point of generalising well over data. This paper presents PLOD, a large-scale dataset for abbreviation detection and extraction that contains 160k+ segments automatically annotated with abbreviations and their long forms. We performed manual validation over a set of instances and a complete automatic validation for this dataset. We then used it to generate several baseline models for detecting abbreviations and long forms. The best models achieved an F1-score of 0.92 for abbreviations and 0.89 for detecting their corresponding long forms. We release this dataset along with our code and all the models publicly at https://github.com/surrey-nlp/PLOD-AbbreviationDetection

In this paper, we present a semi-automated workflow for live interlingual speech-to-text communication which seeks to reduce the shortcomings of existing ASR systems: a human respeaker works with a speaker-dependent speech recognition software (e.g., Dragon Naturally Speaking) to deliver punctuated same-language output of superior quality than obtained using out-of-the-box automatic speech recognition of the original speech. This is fed into a machine translation engine (the EU’s eTranslation) to produce live-caption ready text. We benchmark the quality of the output against the output of best-in-class (human) simultaneous interpreters working with the same source speeches from plenary sessions of the European Parliament. To evaluate the accuracy and facilitate the comparison between the two types of output, we use a tailored annotation approach based on the NTR model (Romero-Fresco and Pöchhacker, 2017). We find that the semi-automated workflow combining intralingual respeaking and machine translation is capable of generating outputs that are similar in terms of accuracy and completeness to the outputs produced in the benchmarking workflow, although the small scale of our experiment requires caution in interpreting this result.

We report the results of the WMT 2022 shared task on Quality Estimation, in which the challenge is to predict the quality of the output of neural machine translation systems at the word and sentence levels, without access to reference translations. This edition introduces a few novel aspects and extensions that aim to enable more fine-grained, and explainable quality estimation approaches. We introduce an updated quality annotation scheme using Multidimensional Quality Metrics to obtain sentence- and word-level quality scores for three language pairs. We also extend the Direct Assessments and post-edit data (MLQE-PE) to new language pairs: we present a novel and large dataset on English-Marathi, as well as a zero-shot test set on English-Yoruba. Further, we include an explainability sub-task for all language pairs and present a new format of a critical error detection task for two new language pairs. Participants from 11 different teams submitted altogether 991 systems to different task variants and language pairs.

This paper summarises the submissions our team, SURREY-CTS-NLP has made for the WASSA 2022 Shared Task for the prediction of empathy, distress and emotion. In this work, we tested different learning strategies, like ensemble learning and multi-task learning, as well as several large language models, but our primary focus was on analysing and extracting emotion-intensive features from both the essays in the training data and the news articles, to better predict empathy and distress scores from the perspective of discourse and sentiment analysis. We propose several text feature extraction schemes to compensate the small size of training examples for fine-tuning pretrained language models, including methods based on Rhetorical Structure Theory (RST) parsing, cosine similarity and sentiment score. Our best submissions achieve an average Pearson correlation score of 0.518 for the empathy prediction task and an F1 score of 0.571 for the emotion prediction task, indicating that using these schemes to extract emotion-intensive information can help improve model performance.

Most studies on word-level Quality Estimation (QE) of machine translation focus on language-specific models. The obvious disadvantages of these approaches are the need for labelled data for each language pair and the high cost required to maintain several language-specific models. To overcome these problems, we explore different approaches to multilingual, word-level QE. We show that multilingual QE models perform on par with the current language-specific models. In the cases of zero-shot and few-shot QE, we demonstrate that it is possible to accurately predict word-level quality for any given new language pair from models trained on other language pairs. Our findings suggest that the word-level QE models based on powerful pre-trained transformers that we propose in this paper generalise well across languages, making them more useful in real-world scenarios.

In translating text where sentiment is the main message, human translators give particular attention to sentiment-carrying words. The reason is that an incorrect translation of such words would miss the fundamental aspect of the source text, i.e. the author’s sentiment. In the online world, MT systems are extensively used to translate User-Generated Content (UGC) such as reviews, tweets, and social media posts, where the main message is often the author’s positive or negative attitude towards the topic of the text. It is important in such scenarios to accurately measure how far an MT system can be a reliable real-life utility in transferring the correct affect message. This paper tackles an under-recognized problem in the field of machine translation evaluation which is judging to what extent automatic metrics concur with the gold standard of human evaluation for a correct translation of sentiment. We evaluate the efficacy of conventional quality metrics in spotting a mistranslation of sentiment, especially when it is the sole error in the MT output. We propose a numerical “sentiment-closeness” measure appropriate for assessing the accuracy of a translated affect message in UGC text by an MT system. We will show that incorporating this sentiment-aware measure can significantly enhance the correlation of some available quality metrics with the human judgement of an accurate translation of sentiment.

Social media companies as well as censorship authorities make extensive use of artificial intelligence (AI) tools to monitor postings of hate speech, celebrations of violence or profanity. Since AI software requires massive volumes of data to train computers, automatic-translation of the online content is usually implemented to compensate for the scarcity of text in some languages. However, machine translation (MT) mistakes are a regular occurrence when translating sentiment-oriented user-generated content (UGC), especially when a low-resource language is involved. In such scenarios, the adequacy of the whole process relies on the assumption that the translation can be evaluated correctly. In this paper, we assess the ability of automatic quality metrics to detect critical machine translation errors which can cause serious misunderstanding of the affect message. We compare the performance of three canonical metrics on meaningless translations as compared to meaningful translations with a critical error that distorts the overall sentiment of the source text. We demonstrate the need for the fine-tuning of automatic metrics to make them more robust in detecting sentiment critical errors.

Current Machine Translation (MT) systems achieve very good results on a growing variety of language pairs and datasets. However, they are known to produce fluent translation outputs that can contain important meaning errors, thus undermining their reliability in practice. Quality Estimation (QE) is the task of automatically assessing the performance of MT systems at test time. Thus, in order to be useful, QE systems should be able to detect such errors. However, this ability is yet to be tested in the current evaluation practices, where QE systems are assessed only in terms of their correlation with human judgements. In this work, we bridge this gap by proposing a general methodology for adversarial testing of QE for MT. First, we show that despite a high correlation with human judgements achieved by the recent SOTA, certain types of meaning errors are still problematic for QE to detect. Second, we show that on average, the ability of a given model to discriminate between meaning-preserving and meaning-altering perturbations is predictive of its overall performance, thus potentially allowing for comparing QE systems without relying on manual quality annotation.

Recent years have seen big advances in the field of sentence-level quality estimation (QE), largely as a result of using neural-based architectures. However, the majority of these methods work only on the language pair they are trained on and need retraining for new language pairs. This process can prove difficult from a technical point of view and is usually computationally expensive. In this paper we propose a simple QE framework based on cross-lingual transformers, and we use it to implement and evaluate two different neural architectures. Our evaluation shows that the proposed methods achieve state-of-the-art results outperforming current open-source quality estimation frameworks when trained on datasets from WMT. In addition, the framework proves very useful in transfer learning settings, especially when dealing with low-resourced languages, allowing us to obtain very competitive results.

This paper presents the team TransQuest’s participation in Sentence-Level Direct Assessment shared task in WMT 2020. We introduce a simple QE framework based on cross-lingual transformers, and we use it to implement and evaluate two different neural architectures. The proposed methods achieve state-of-the-art results surpassing the results obtained by OpenKiwi, the baseline used in the shared task. We further fine tune the QE framework by performing ensemble and data augmentation. Our approach is the winning solution in all of the language pairs according to the WMT 2020 official results.

This paper presents the RGCL team submission to SemEval 2020 Task 6: DeftEval, subtasks 1 and 2. The system classifies definitions at the sentence and token levels. It utilises state-of-the-art neural network architectures, which have some task-specific adaptations, including an automatically extended training set. Overall, the approach achieves acceptable evaluation scores, while maintaining flexibility in architecture selection.

Matching and retrieving previously translated segments from the Translation Memory is a key functionality in Translation Memories systems. However this matching and retrieving process is still limited to algorithms based on edit distance which we have identified as a major drawback in Translation Memories systems. In this paper, we introduce sentence encoders to improve matching and retrieving process in Translation Memories systems - an effective and efficient solution to replace edit distance-based algorithms.

One very common type of fake news is satire which comes in a form of a news website or an online platform that parodies reputable real news agencies to create a sarcastic version of reality. This type of fake news is often disseminated by individuals on their online platforms as it has a much stronger effect in delivering criticism than through a straightforward message. However, when the satirical text is disseminated via social media without mention of its source, it can be mistaken for real news. This study conducts several exploratory analyses to identify the linguistic properties of Arabic fake news with satirical content. It shows that although it parodies real news, Arabic satirical news has distinguishing features on the lexico-grammatical level. We exploit these features to build a number of machine learning models capable of identifying satirical fake news with an accuracy of up to 98.6%. The study introduces a new dataset (3185 articles) scraped from two Arabic satirical news websites (‘Al-Hudood’ and ‘Al-Ahram Al-Mexici’) which consists of fake news. The real news dataset consists of 3710 articles collected from three official news sites: the ‘BBC-Arabic’, the ‘CNN-Arabic’ and ‘Al-Jazeera news’. Both datasets are concerned with political issues related to the Middle East.

Since the advent of Neural Machine Translation (NMT) approaches there has been a tremendous improvement in the quality of automatic translation. However, NMT output still lacks accuracy in some low-resource languages and sometimes makes major errors that need extensive postediting. This is particularly noticeable with texts that do not follow common lexico-grammatical standards, such as user generated content (UGC). In this paper we investigate the challenges involved in translating book reviews from Arabic into English, with particular focus on the errors that lead to incorrect translation of sentiment polarity. Our study points to the special characteristics of Arabic UGC, examines the sentiment transfer errors made by Google Translate of Arabic UGC to English, analyzes why the problem occurs, and proposes an error typology specific of the translation of Arabic UGC. Our analysis shows that the output of online translation tools of Arabic UGC can either fail to transfer the sentiment at all by producing a neutral target text, or completely flips the sentiment polarity of the target word or phrase and hence delivers a wrong affect message. We address this problem by fine-tuning an NMT model with respect to sentiment polarity showing that this approach can significantly help with correcting sentiment errors detected in the online translation of Arabic UGC.

This article describes the system submitted by the RGCL-WLV team to the SemEval 2019 Task 12: Toponym resolution in scientific papers. The system detects toponyms using a bootstrapped machine learning (ML) approach which classifies names identified using gazetteers extracted from the GeoNames geographical database. The paper evaluates the performance of several ML classifiers, as well as how the gazetteers influence the accuracy of the system. Several runs were submitted. The highest precision achieved for one of the submissions was 89%, albeit it at a relatively low recall of 49%.

In this paper, we report on the extrinsic evaluation of an automatic sentence simplification method with respect to two NLP tasks: semantic role labelling (SRL) and information extraction (IE). The paper begins with our observation of challenges in the intrinsic evaluation of sentence simplification systems, which motivates the use of extrinsic evaluation of these systems with respect to other NLP tasks. We describe the two NLP systems and the test data used in the extrinsic evaluation, and present arguments and evidence motivating the integration of a sentence simplification step as a means of improving the accuracy of these systems. Our evaluation reveals that their performance is improved by the simplification step: the SRL system is better able to assign semantic roles to the majority of the arguments of verbs and the IE system is better able to identify fillers for all IE template slots.

This paper compares how different machine learning classifiers can be used together with simple string matching and named entity recognition to detect locations in texts. We compare five different state-of-the-art machine learning classifiers in order to predict whether a sentence contains a location or not. Following this classification task, we use a string matching algorithm with a gazetteer to identify the exact index of a toponym within the sentence. We evaluate different approaches in terms of machine learning classifiers, text pre-processing and location extraction on the SemEval-2019 Task 12 dataset, compiled for toponym resolution in the bio-medical domain. Finally, we compare the results with our system that was previously submitted to the SemEval-2019 task evaluation.

Calculating Semantic Textual Similarity (STS) plays a significant role in many applications such as question answering, document summarisation, information retrieval and information extraction. All modern state of the art STS methods rely on word embeddings one way or another. The recently introduced contextualised word embeddings have proved more effective than standard word embeddings in many natural language processing tasks. This paper evaluates the impact of several contextualised word embeddings on unsupervised STS methods and compares it with the existing supervised/unsupervised STS methods for different datasets in different languages and different domains

Calculating the Semantic Textual Similarity (STS) is an important research area in natural language processing which plays a significant role in many applications such as question answering, document summarisation, information retrieval and information extraction. This paper evaluates Siamese recurrent architectures, a special type of neural networks, which are used here to measure STS. Several variants of the architecture are compared with existing methods

NLP approaches to automatic text adaptation often rely on user-need guidelines which are generic and do not account for the differences between various types of target groups. One such group are adults with high-functioning autism, who are usually able to read long sentences and comprehend difficult words but whose comprehension may be impeded by other linguistic constructions. This is especially challenging for real-world user-generated texts such as product reviews, which cannot be controlled editorially and are thus a particularly good applcation for automatic text adaptation systems. In this paper we present a mixed-methods survey conducted with 24 adult web-users diagnosed with autism and an age-matched control group of 33 neurotypical participants. The aim of the survey was to identify whether the group with autism experienced any barriers when reading online reviews, what these potential barriers were, and what NLP methods would be best suited to improve the accessibility of online reviews for people with autism. The group with autism consistently reported significantly greater difficulties with understanding online product reviews compared to the control group and identified issues related to text length, poor topic organisation, and the use of irony and sarcasm.

This paper describes our participation in the First Shared Task on Aggression Identification. The method proposed relies on machine learning to identify social media texts which contain aggression. The main features employed by our method are information extracted from word embeddings and the output of a sentiment analyser. Several machine learning methods and different combinations of features were tried. The official submissions used Support Vector Machines and Random Forests. The official evaluation showed that for texts similar to the ones in the training dataset Random Forests work best, whilst for texts which are different SVMs are a better choice. The evaluation also showed that despite its simplicity the method performs well when compared with more elaborated methods.

Detecting stress from social media gives a non-intrusive and inexpensive alternative to traditional tools such as questionnaires or physiological sensors for monitoring mental state of individuals. This paper introduces a novel framework for finding reasons for stress from tweets, analyzing multiple categories for the first time. Three word-vector based methods are evaluated on collections of tweets about politics or airlines and are found to be more accurate than standard machine learning algorithms.

Given the lack of large user-evaluated corpora in disability-related NLP research (e.g. text simplification or readability assessment for people with cognitive disabilities), the question of choosing suitable training data for NLP models is not straightforward. The use of large generic corpora may be problematic because such data may not reflect the needs of the target population. The use of the available user-evaluated corpora may be problematic because these datasets are not large enough to be used as training data. In this paper we explore a third approach, in which a large generic corpus is combined with a smaller population-specific corpus to train a classifier which is evaluated using two sets of unseen user-evaluated data. One of these sets, the ASD Comprehension corpus, is developed for the purposes of this study and made freely available. We explore the effects of the size and type of the training data used on the performance of the classifiers, and the effects of the type of the unseen test datasets on the classification performance.

We present an extension of the coreference annotation in the English NP4E and the Catalan AnCora-CA corpora with near-identity relations, which are borderline cases of coreference. The annotated subcorpora have 50K tokens each. Near-identity relations, as presented by Recasens et al. (2010; 2011), build upon the idea that identity is a continuum rather than an either/or relation, thus introducing a middle ground category to explain currently problematic cases. The first annotation effort that we describe shows that it is not possible to annotate near-identity explicitly because subjects are not fully aware of it. Therefore, our second annotation effort used an indirect method, and arrived at near-identity annotations by inference from the disagreements between five annotators who had only a two-alternative choice between coreference and non-coreference. The results show that whereas as little as 2-6% of the relations were explicitly annotated as near-identity in the former effort, up to 12-16% of the relations turned out to be near-identical following the indirect method of the latter effort.

Due to the increasing number of emergency situations which can have substantial consequences, both financially and fatally, the Crisis Management (CM) domain is developing at an exponential speed. The efficient management of emergency situations relies on clear communication between all of the participants in a crisis situation. For these reasons the Text Complexity (TC) of the CM domain needed to be investigated and showed that CM domain texts exhibit high TC levels. This article presents a new linguistic resource in the form of Controlled Language (CL) guidelines for manual text simplification in the CM domain which aims to address high TC in the CM domain and produce clear messages to be used in crisis situations. The effectiveness of the resource has been tested via evaluation from several different perspectives important for the domain. The overall results show that the CLCM simplification has a positive impact on TC, reading comprehension, manual translation and machine translation. Additionally, an investigation of the cognitive difficulty in applying manual simplification operations led to interesting discoveries. This article provides details of the evaluation methods, the conducted experiments, their results and indications about future work.

The rapid growth of the Internet means that more information is available than ever before. Multilingual multi-document summarisation offers a way to access this information even when it is not in a language spoken by the reader by extracting the gist from related documents and translating it automatically. This paper presents an experiment in which Maximal Marginal Relevance (MMR), a well known multi-document summarisation method, is used to produce summaries from Romanian news articles. A task-based evaluation performed on both the original summaries and on their automatically translated versions reveals that they still contain a significant portion of the important information from the original texts. However, direct evaluation of the automatically translated summaries shows that they are not very legible and this can put off some readers who want to find out more about a topic.

With the appearance of Semantic Web technologies, it becomes possible to develop novel, sophisticated question answering systems, where ontologies are usually used as the core knowledge component. In the EU-funded project, QALL-ME, a domain-specific ontology was developed and applied for question answering in the domain of tourism, along with the assistance of two upper ontologies for concept expansion and reasoning. This paper focuses on the development of the QALL-ME ontology in the tourism domain and its alignment with the upper ontologies - WordNet and SUMO. The design of the ontology is presented in the paper, and a semi-automatic alignment procedure is described with some alignment results given as well. Furthermore, the aligned ontology was used to semantically annotate original data obtained from the tourism web sites and natural language questions. The storage schema of the annotated data and the data access method for retrieving answers from the annotated data are also reported in the paper.

This paper presents the QALL-ME benchmark, a multilingual resource of annotated spoken requests in the tourism domain, freely available for research purposes. The languages currently involved in the project are Italian, English, Spanish and German. It introduces a semantic annotation scheme for spoken information access requests, specifically derived from Question Answering (QA) research. In addition to pragmatic and semantic annotations, we propose three QA-based annotation levels: the Expected Answer Type, the Expected Answer Quantifier and the Question Topical Target of a request, to fully capture the content of a request and extract the sought-after information. The QALL-ME benchmark is developed under the EU-FP6 QALL-ME project which aims at the realization of a shared and distributed infrastructure for Question Answering (QA) systems on mobile devices (e.g. mobile phones). Questions are formulated by the users in free natural language input, and the system returns the actual sequence of words which constitutes the answer from a collection of information sources (e.g. documents, databases). Within this framework, the benchmark has the twofold purpose of training machine learning based applications for QA, and testing their actual performance with a rapid turnaround in controlled laboratory setting.

Evaluation campaigns have become an established way to evaluate automatic systems which tackle the same task. This paper presents the first edition of the Anaphora Resolution Exercise (ARE) and the lessons learnt from it. This first edition focused only on English pronominal anaphora and NP coreference, and was organised as an exploratory exercise where various issues were investigated. ARE proposed four different tasks: pronominal anaphora resolution and NP coreference resolution on a predefined set of entities, pronominal anaphora resolution and NP coreference resolution on raw texts. For each of these tasks different inputs and evaluation metrics were prepared. This paper presents the four tasks, their input data and evaluation metrics used. Even though a large number of researchers in the field expressed their interest to participate, only three institutions took part in the formal evaluation. The paper briefly presents their results, but does not try to interpret them because in this edition of ARE our aim was not about finding why certain methods are better, but to prepare the ground for a fully-fledged edition.

Computer-aided summarisation is a technology developed at the University of Wolverhampton as a complement to automatic summarisation, to produce high quality summaries with less effort. To achieve this, a user-friendly environment which incorporates several well-known summarisation methods has been developed. This paper presents the main features of the computer-aided summarisation environment and explains the changes introduced to it as a result of user feedback.

This paper investigates the problem of automatically annotating resources with NP coreference information using a parallel corpus, English-Romanian, in order to transfer, through word alignment, coreference chains from the English part to the Romanian part of the corpus. The results show that we can detect Romanian referential expressions and coreference chains with over 80% F-measure, thus using our method as a preprocessing step followed by manual correction as part of an annotation effort for creating a large Romanian corpus with coreference information is worthwhile.

This paper describes a pilot project which developed a methodology for NP and event coreference annotation consisting of detailed annotation schemes and guidelines. In order to develop this, a small sample annotated corpus in the domain of terrorism/security was built. The methodology developed can be used as a basis for large-scale annotation to produce much-needed resources. In contrast to related projects, ours focused almost exclusively on the development of annotation guidelines and schemes, to ensure that future annotations based on this methodology capture the phenomena both reliably and in detail. The project also involved extensive discussions in order to redraft the guidelines, as well as major extensions to PALinkA, our existing annotation tool, to accommodate event as well as NP coreference annotation.