Depression is a highly prevalent condition recognized by the World Health Organization as a leading contributor to global disability. Many people suffering from depression express their thoughts and feelings using social media, which thus becomes a source of data for research in this domain. However, existing annotation schemes tailored to studying depression symptoms in social media data remain limited. Reliable and valid annotation guidelines are crucial for accurately measuring mental health conditions for those studies. This paper addresses this gap by presenting a novel depression annotation scheme and guidelines for detecting depression symptoms and their severity in social media text. Our approach leverages validated depression questionnaires and incorporates the expertise of psychologists and psychiatrists during scheme refinement. The resulting annotation scheme achieves high inter-rater agreement, demonstrating its potential for suitable depression assessment in social media contexts.

Social Anxiety Disorder (SAD) is a common condition, affecting a significant portion of the population. While research suggests spending time in nature can alleviate anxiety, the specific impact on SAD remains unclear. This study explores the relationship between discussions of outdoor spaces and social anxiety on social media. We leverage transformer-based and large language models (LLMs) to analyze a social media dataset focused on SAD. We developed three methods for the task of predicting the effects of outdoor spaces on SAD in social media. A two-stage pipeline classifier achieved the best performance of our submissions with results exceeding baseline performance.

For numerous years, researchers have employed social media data to gain insights into users’ mental health. Nevertheless, the majority of investigations concentrate on categorizing users into those experiencing depression and those considered healthy, or on detection of suicidal thoughts. In this paper, we aim to extract evidence of a pre-assigned gold label. We used a suicidality dataset containing Reddit posts labeled with the suicide risk level. The task is to use Large Language Models (LLMs) to extract evidence from the post that justifies the given label. We used Meta Llama 7b and lexicons for solving the task and we achieved a precision of 0.96.

Code clone detection is challenging, as sourcecode can be written in different languages, do-mains, and styles. In this paper, we arguethat source code is inherently a graph, not asequence, and that graph-based methods aremore suitable for code clone detection thansequence-based methods. We compare the per-formance of two state-of-the-art models: Code-BERT (Feng et al., 2020), a sequence-basedmodel, and CodeGraph (Yu et al., 2023), agraph-based model, on two benchmark data-sets: BCB (Svajlenko et al., 2014) and PoolC(PoolC, no date). We show that CodeGraphoutperforms CodeBERT on both data-sets, es-pecially on cross-lingual code clones. To thebest of our knowledge, this is the first work todemonstrate the cross-lingual code clone detec-tion showing superiority on graph-based meth-ods over sequence-based methods

We introduce a hybrid abstractive summarisation approach combining hierarchical VAEs with LLMs to produce clinically meaningful summaries from social media user timelines, appropriate for mental health monitoring. The summaries combine two different narrative points of view: (a) clinical insights in third person, generated by feeding into an LLM clinical expert-guided prompts, and importantly, (b) a temporally sensitive abstractive summary of the user’s timeline in first person, generated by a novel hierarchical variational autoencoder, TH-VAE. We assess the generated summaries via automatic evaluation against expert summaries and via human evaluation with clinical experts, showing that timeline summarisation by TH-VAE results in more factual and logically coherent summaries rich in clinical utility and superior to LLM-only approaches in capturing changes over time.

Mental illness can significantly impact individuals’ quality of life. Analysing social media data to uncover potential mental health issues in individuals via their posts is a popular research direction. However, most studies focus on the classification of users suffering from depression versus healthy users, or on the detection of suicidal thoughts. In this paper, we instead aim to understand and model linguistic changes that occur when users transition from a healthy to an unhealthy state. Addressing this gap could lead to better approaches for earlier depression detection when signs are not as obvious as in cases of severe depression or suicidal ideation. In order to achieve this goal, we have collected the first dataset of textual posts by the same users before and after reportedly being diagnosed with depression. We then use this data to build multiple predictive models (based on SVM, Random Forests, BERT, RoBERTa, MentalBERT, GPT-3, GPT-3.5, Bard, and Alpaca) for the task of classifying user posts. Transformer-based models achieved the best performance, while large language models used off-the-shelf proved less effective as they produced random guesses (GPT and Bard) or hallucinations (Alpaca).

The performance of Reinforcement Learning (RL) for natural language tasks including Machine Translation (MT) is crucially dependent on the reward formulation. This is due to the intrinsic difficulty of the task in the high-dimensional discrete action space as well as the sparseness of the standard reward functions defined for limited set of ground-truth sequences biased towards singular lexical choices. To address this issue, we formulate SURF, a maximally dense semantic-level unsupervised reward function which mimics human evaluation by considering both sentence fluency and semantic similarity. We demonstrate the strong potential of SURF to leverage a family of Actor-Critic Transformer-based Architectures with synchronous and asynchronous multi-agent variants. To tackle the problem of large action-state spaces, each agent is equipped with unique exploration strategies, promoting diversity during its exploration of the hypothesis space. When BLEU scores are compared, our dense unsupervised reward outperforms the standard sparse reward by 2% on average for in- and out-of-domain settings.

This paper reports the results of the shared task we hosted on the Third Workshop of Automatic Simultaneous Translation (AutoSimTrans). The shared task aims to promote the development of text-to-text and speech-to-text simultaneous translation, and includes Chinese-English and English-Spanish tracks. The number of systems submitted this year has increased fourfold compared with last year. Additionally, the top 1 ranked system in the speech-to-text track is the first end-to-end submission we have received in the past three years, which has shown great potential. This paper reports the results and descriptions of the 14 participating teams, compares different evaluation metrics, and revisits the ranking method.

Social media data have been used in research for many years to understand users’ mental health. In this paper, using user-generated content we aim to achieve two goals: the first is detecting moments of mood change over time using timelines of users from Reddit. The second is predicting the degree of suicide risk as a user-level classification task. We used different approaches to address longitudinal modelling as well as the problem of the severely imbalanced dataset. Using BERT with undersampling techniques performed the best among other LSTM and basic random forests models for the first task. For the second task, extracting some features related to suicide from posts’ text contributed to the overall performance improvement. Specifically, a number of suicide-related words in a post as a feature improved the accuracy by 17%.

Reinforcement Learning (RL) is a powerful framework to address the discrepancy between loss functions used during training and the final evaluation metrics to be used at test time. When applied to neural Machine Translation (MT), it minimises the mismatch between the cross-entropy loss and non-differentiable evaluation metrics like BLEU. However, the suitability of these metrics as reward function at training time is questionable: they tend to be sparse and biased towards the specific words used in the reference texts. We propose to address this problem by making models less reliant on such metrics in two ways: (a) with an entropy-regularised RL method that does not only maximise a reward function but also explore the action space to avoid peaky distributions; (b) with a novel RL method that explores a dynamic unsupervised reward function to balance between exploration and exploitation. We base our proposals on the Soft Actor-Critic (SAC) framework, adapting the off-policy maximum entropy model for language generation applications such as MT. We demonstrate that SAC with BLEU reward tends to overfit less to the training data and performs better on out-of-domain data. We also show that our dynamic unsupervised reward can lead to better translation of ambiguous words.

This paper addresses the problem of simultaneous machine translation (SiMT) by exploring two main concepts: (a) adaptive policies to learn a good trade-off between high translation quality and low latency; and (b) visual information to support this process by providing additional (visual) contextual information which may be available before the textual input is produced. For that, we propose a multimodal approach to simultaneous machine translation using reinforcement learning, with strategies to integrate visual and textual information in both the agent and the environment. We provide an exploration on how different types of visual information and integration strategies affect the quality and latency of simultaneous translation models, and demonstrate that visual cues lead to higher quality while keeping the latency low.

Contextualised word embeddings is a powerful tool to detect contextual synonyms. However, most of the current state-of-the-art (SOTA) deep learning concept extraction methods remain supervised and underexploit the potential of the context. In this paper, we propose a self-supervised pre-training approach which is able to detect contextual synonyms of concepts being training on the data created by shallow matching. We apply our methodology in the sparse multi-class setting (over 15,000 concepts) to extract phenotype information from electronic health records. We further investigate data augmentation techniques to address the problem of the class sparsity. Our approach achieves a new SOTA for the unsupervised phenotype concept annotation on clinical text on F1 and Recall outperforming the previous SOTA with a gain of up to 4.5 and 4.0 absolute points, respectively. After fine-tuning with as little as 20% of the labelled data, we also outperform BioBERT and ClinicalBERT. The extrinsic evaluation on three ICU benchmarks also shows the benefit of using the phenotypes annotated by our model as features.

We introduce a machine translation dataset for three pairs of languages in the legal domain with post-edited high-quality neural machine translation and independent human references. The data was collected as part of the EU APE-QUEST project and comprises crawled content from EU websites with translation from English into three European languages: Dutch, French and Portuguese. Altogether, the data consists of around 31K tuples including a source sentence, the respective machine translation by a neural machine translation system, a post-edited version of such translation by a professional translator, and - where available - the original reference translation crawled from parallel language websites. We describe the data collection process, provide an analysis of the resulting post-edits and benchmark the data using state-of-the-art quality estimation and automatic post-editing models. One interesting by-product of our post-editing analysis suggests that neural systems built with publicly available general domain data can provide high-quality translations, even though comparison to human references suggests that this quality is quite low. This makes our dataset a suitable candidate to test evaluation metrics. The data is freely available as an ELRC-SHARE resource.

Natural Language Processing (NLP) can help unlock the vast troves of unstructured data in clinical text and thus improve healthcare research. However, a big barrier to developments in this field is data access due to patient confidentiality which prohibits the sharing of this data, resulting in small, fragmented and sequestered openly available datasets. Since NLP model development requires large quantities of data, we aim to help side-step this roadblock by exploring the usage of Natural Language Generation in augmenting datasets such that they can be used for NLP model development on downstream clinically relevant tasks. We propose a methodology guiding the generation with structured patient information in a sequence-to-sequence manner. We experiment with state-of-the-art Transformer models and demonstrate that our augmented dataset is capable of beating our baselines on a downstream classification task. Finally, we also create a user interface and release the scripts to train generation models to stimulate further research in this area.

While Dementia with Lewy Bodies (DLB) is the second most common type of neurodegenerative dementia following Alzheimer’s Disease (AD), it is difficult to distinguish from AD. We propose a method for DLB detection by using mental health record (MHR) documents from a (3-month) period before a patient has been diagnosed with DLB or AD. Our objective is to develop a model that could be clinically useful to differentiate between DLB and AD across datasets from different healthcare institutions. We cast this as a classification task using Convolutional Neural Network (CNN), an efficient neural model for text classification. We experiment with different representation models, and explore the features that contribute to model performances. In addition, we apply temperature scaling, a simple but efficient model calibration method, to produce more reliable predictions. We believe the proposed method has important potential for clinical applications using routine healthcare records, and for generalising to other relevant clinical record datasets. To the best of our knowledge, this is the first attempt to distinguish DLB from AD using mental health records, and to improve the reliability of DLB predictions.

Simultaneous machine translation (SiMT) aims to translate a continuous input text stream into another language with the lowest latency and highest quality possible. The translation thus has to start with an incomplete source text, which is read progressively, creating the need for anticipation. In this paper, we seek to understand whether the addition of visual information can compensate for the missing source context. To this end, we analyse the impact of different multimodal approaches and visual features on state-of-the-art SiMT frameworks. Our results show that visual context is helpful and that visually-grounded models based on explicit object region information are much better than commonly used global features, reaching up to 3 BLEU points improvement under low latency scenarios. Our qualitative analysis illustrates cases where only the multimodal systems are able to translate correctly from English into gender-marked languages, as well as deal with differences in word order, such as adjective-noun placement between English and French.

Previous work on multimodal machine translation has shown that visual information is only needed in very specific cases, for example in the presence of ambiguous words where the textual context is not sufficient. As a consequence, models tend to learn to ignore this information. We propose a translate-and-refine approach to this problem where images are only used by a second stage decoder. This approach is trained jointly to generate a good first draft translation and to improve over this draft by (i) making better use of the target language textual context (both left and right-side contexts) and (ii) making use of visual context. This approach leads to the state of the art results. Additionally, we show that it has the ability to recover from erroneous or missing words in the source language.

This paper describes the cascaded multimodal speech translation systems developed by Imperial College London for the IWSLT 2019 evaluation campaign. The architecture consists of an automatic speech recognition (ASR) system followed by a Transformer-based multimodal machine translation (MMT) system. While the ASR component is identical across the experiments, the MMT model varies in terms of the way of integrating the visual context (simple conditioning vs. attention), the type of visual features exploited (pooled, convolutional, action categories) and the underlying architecture. For the latter, we explore both the canonical transformer and its deliberation version with additive and cascade variants which differ in how they integrate the textual attention. Upon conducting extensive experiments, we found that (i) the explored visual integration schemes often harm the translation performance for the transformer and additive deliberation, but considerably improve the cascade deliberation; (ii) the transformer and cascade deliberation integrate the visual modality better than the additive deliberation, as shown by the incongruence analysis.

Most text-to-text generation tasks, for example text summarisation and text simplification, require copying words from the input to the output. We introduce Copycat, a transformer-based pointer network for such tasks which obtains competitive results in abstractive text summarisation and generates more abstractive summaries. We propose a further extension of this architecture for automatic post-editing, where generation is conditioned over two inputs (source language and machine translation), and the model is capable of deciding where to copy information from. This approach achieves competitive performance when compared to state-of-the-art automated post-editing systems. More importantly, we show that it addresses a well-known limitation of automatic post-editing - overcorrecting translations - and that our novel mechanism for copying source language words improves the results.

A major obstacle to the development of Natural Language Processing (NLP) methods in the biomedical domain is data accessibility. This problem can be addressed by generating medical data artificially. Most previous studies have focused on the generation of short clinical text, and evaluation of the data utility has been limited. We propose a generic methodology to guide the generation of clinical text with key phrases. We use the artificial data as additional training data in two key biomedical NLP tasks: text classification and temporal relation extraction. We show that artificially generated training data used in conjunction with real training data can lead to performance boosts for data-greedy neural network algorithms. We also demonstrate the usefulness of the generated data for NLP setups where it fully replaces real training data.

Mental health problems represent a major public health challenge. Automated analysis of text related to mental health is aimed to help medical decision-making, public health policies and to improve health care. Such analysis may involve text classification. Traditionally, automated classification has been performed mainly using machine learning methods involving costly feature engineering. Recently, the performance of those methods has been dramatically improved by neural methods. However, mainly Convolutional neural networks (CNNs) have been explored. In this paper, we apply a hierarchical Recurrent neural network (RNN) architecture with an attention mechanism on social media data related to mental health. We show that this architecture improves overall classification results as compared to previously reported results on the same data. Benefitting from the attention mechanism, it can also efficiently select text elements crucial for classification decisions, which can also be used for in-depth analysis.

In this paper we present the University of Sheffield submissions for the WMT18 Quality Estimation shared task. We discuss our submissions to all four sub-tasks, where ours is the only team to participate in all language pairs and variations (37 combinations). Our systems show competitive results and outperform the baseline in nearly all cases.

Predicting Machine Translation (MT) quality can help in many practical tasks such as MT post-editing. The performance of Quality Estimation (QE) methods has drastically improved recently with the introduction of neural approaches to the problem. However, thus far neural approaches have only been designed for word and sentence-level prediction. We present a neural framework that is able to accommodate neural QE approaches at these fine-grained levels and generalize them to the level of documents. We test the framework with two sentence-level neural QE approaches: a state of the art approach that requires extensive pre-training, and a new light-weight approach that we propose, which employs basic encoders. Our approach is significantly faster and yields performance improvements for a range of document-level quality estimation tasks. To our knowledge, this is the first neural architecture for document-level QE. In addition, for the first time we apply QE models to the output of both statistical and neural MT systems for a series of European languages and highlight the new challenges resulting from the use of neural MT.

Sentence compression is a way to perform text simplification and is usually handled in a monolingual setting. In this paper, we study ways to extend sentence compression in a bilingual context, where the goal is to obtain parallel compressions of parallel sentences. This can be beneficial for a series of multilingual natural language processing (NLP) tasks. We compare two ways to take bilingual information into account when compressing parallel sentences. Their efficiency is contrasted on a parallel corpus of News articles.