Miracle-dz/UniFAR-FTUs huggingface dataset:
No Stock is an Island: Learning Internal and Relational Attributes of Stocks with Contrastive Learning

Previous work has demonstrated the viability of applying deep learning techniques in the financial area. Recently, the task of stock embedding learning has been drawing attention from the research community, which aims to represent the characteristics of stocks with distributed vectors that can be used in various financial analysis scenarios. Existing approaches for learning stock embeddings either require expert knowledge, or mainly focus on the textual part of information corresponding to individual temporal movements. In this paper, we propose to model stock properties as the combination of internal attributes and relational attributes, which takes into consideration both the time-invariant properties of individual stocks and their movement patterns in relation to the market. To learn the two types of attributes from financial news and transaction data, we design several training objectives based on contrastive learning to extract and separate the long-term and temporary information in the data that are able to counter the inherent randomness of the stock market. Experiments and further analyses on portfolio optimization reveal the effectiveness of our method in extracting comprehensive stock information from various data sources.
Building a Clinically-Focused Problem List From Medical Notes

Clinical notes often contain useful information not documented in structured data, but their unstructured nature can lead to critical patient-related information being missed. To increase the likelihood that this valuable information is utilized for patient care, algorithms that summarize notes into a problem list have been proposed. Focused on identifying medically-relevant entities in the free-form text, these solutions are often detached from a canonical ontology and do not allow downstream use of the detected text-spans. Mitigating these issues, we present here a system for generating a canonical problem list from medical notes, consisting of two major stages. At the first stage, annotation, we use a transformer model to detect all clinical conditions which are mentioned in a single note. These clinical conditions are then grounded to a predefined ontology, and are linked to spans in the text. At the second stage, summarization, we develop a novel algorithm that aggregates over the set of clinical conditions detected on all of the patient’s notes, and produce a concise patient summary that organizes their most important conditions.
Automatic Term and Sentence Classification Via Augmented Term and Pre-trained language model in ESG Taxonomy texts

In this paper, we present our solutions to the FinSim4 Shared Task which is co-located with the FinNLP workshop at IJCAI-2022. This new edition of FinSim4-ESG is extended to the “Environment, Social and Governance (ESG)” related issues in the financial domain. There are two sub-tasks in the FinSim4 shared task. The goal of sub-task1 is to develop a model to predict correctly a list of given terms from ESG taxonomy domain into the most relevant concepts. The aim of subtask2 is to design a system that can automatically classify the ESG Taxonomy text sentence into sustainable or unsustainable class. We have developed different classifiers to automatically classify the terms and sentences with augmented term and pre-trained language models: tf-idf vector, word2vec, Bert, Distill-Bert, Albert, Roberta. The result dashboard shows that our proposed methods yield a significant performance improvement compared to the baseline which ranked 1st in the subtask2 and 2rd of mean rank in the subtask1.
A Dataset of Sustainable Diet Arguments on Twitter

Sustainable development requires a significant change in our dietary habits. Argument mining can help achieve this goal by both affecting and helping understand people’s behavior. We design an annotation scheme for argument mining from online discourse around sustainable diets, including novel evidence types specific to this domain. Using Twitter as a source, we crowdsource a dataset of 597 tweets annotated in relation to 5 topics. We benchmark a variety of NLP models on this dataset, demonstrating strong performance in some sub-tasks, while highlighting remaining challenges.
Tracking Semantic Shifts in German Court Decisions with Diachronic Word Embeddings

Language and its usage change over time. While legal language is arguably more stable than everyday language, it is still subject to change. Sometimes it changes gradually and slowly, sometimes almost instantaneously, for example through legislative changes. This paper presents an application of diachronic word embeddings to track changes in the usage of language by German courts triggered by changing legislation, based on a corpus of more than 200,000 documents. The results show the swift and lasting effect that changes in legislation can have on the usage of language by courts and suggest that using time-restricted word embedding models could be beneficial for downstream NLP tasks.
MaTESe: Machine Translation Evaluation as a Sequence Tagging Problem

Starting from last year, WMT human evaluation has been performed within the Multidimensional Quality Metrics (MQM) framework, where human annotators are asked to identify error spans in translations, alongside an error category and a severity. In this paper, we describe our submission to the WMT 2022 Metrics Shared Task, where we propose using the same paradigm for automatic evaluation: we present the MaTESe metrics, which reframe machine translation evaluation as a sequence tagging problem. Our submission also includes a reference-free metric, denominated MaTESe-QE. Despite the paucity of the openly available MQM data, our metrics obtain promising results, showing high levels of correlation with human judgements, while also enabling an evaluation that is interpretable. Moreover, MaTESe-QE can also be employed in settings where it is infeasible to curate reference translations manually.
PRHLT’s Submission to WLAC 2022

This paper describes our submission to the Word-Level AutoCompletion shared task of WMT22. We participated in the English–German and German–English categories. We proposed a segment-based interactive machine translation approach whose central core is a machine translation (MT) model which generates a complete translation from the context provided by the task. From there, we obtain the word which corresponds to the autocompletion. With this approach, we aim to show that it is possible to use the MT models in the autocompletion task by simply performing minor changes at the decoding step, obtaining satisfactory results.
Disentangled Variational Topic Inference for Topic-Accurate Financial Report Generation

Automatic generating financial report from a set of news is important but challenging. The financial reports is composed of key points of the news and corresponding inferring and reasoning from specialists in financial domain with professional knowledge. The challenges lie in the effective learning of the extra knowledge that is not well presented in the news, and the misalignment between topic of input news and output knowledge in target reports. In this work, we introduce a disentangled variational topic inference approach to learn two latent variables for news and report, respectively. We use a publicly available dataset to evaluate the proposed approach. The results demonstrate its effectiveness of enhancing the language informativeness and the topic accuracy of the generated financial reports.
Named Entity Recognition as Structured Span Prediction

Named Entity Recognition (NER) is an important task in Natural Language Processing with applications in many domains. While the dominant paradigm of NER is sequence labelling, span-based approaches have become very popular in recent times but are less well understood. In this work, we study different aspects of span-based NER, namely the span representation, learning strategy, and decoding algorithms to avoid span overlap. We also propose an exact algorithm that efficiently finds the set of non-overlapping spans that maximizes a global score, given a list of candidate spans. We performed our study on three benchmark NER datasets from different domains. We make our code publicly available at https://github.com/urchade/span-structured-prediction.
IIT Bombay’s WMT22 Automatic Post-Editing Shared Task Submission

This paper describes IIT Bombay’s submission to the WMT22 Automatic Post-Editing (APE) shared task for the English-Marathi (En-Mr) language pair. We follow the curriculum training strategy to train our APE system. First, we train an encoder-decoder model to perform translation from English to Marathi. Next, we add another encoder to the model and train the resulting {textit{dual-encoder single-decoder} model for the APE task. This involves training the model using the synthetic APE data in multiple training stages and then fine-tuning it using the real APE data. We use the LaBSE technique to ensure the quality of the synthetic APE data. For data augmentation, along with using candidates obtained from an external machine translation (MT) system, we also use the phrase-level APE triplets generated using phrase table injection. As APE systems are prone to the problem of ‘over-correction’, we use a sentence-level quality estimation (QE) system to select the final output between an original translation and the corresponding output generated by the APE model. Our approach improves the TER and BLEU scores on the development set by -3.92 and +4.36 points, respectively. Also, the final results on the test set show that our APE system outperforms the baseline system by -3.49 TER points and +5.37 BLEU points.
The ARC-NKUA Submission for the English-Ukrainian General Machine Translation Shared Task at WMT22

The ARC-NKUA (“Athena” Research Center - National and Kapodistrian University of Athens) submission to the WMT22 General Machine Translation shared task concerns the unconstrained tracks of the English-Ukrainian and Ukrainian-English translation directions. The two Neural Machine Translation systems are based on Transformer models and our primary submissions were determined through experimentation with (a) ensemble decoding, (b) selected fine-tuning with a subset of the training data, (c) data augmentation with back-translated monolingual data, and (d) post-processing of the translation outputs. Furthermore, we discuss filtering techniques and the acquisition of additional data used for training the systems.
Investigating Effectiveness of Multi-Encoder for Conversational Neural Machine Translation

Multilingual chatbots are the need of the hour for modern business. There is increasing demand for such systems all over the world. A multilingual chatbot can help to connect distant parts of the world together, without sharing a common language. We participated in WMT22 Chat Translation Shared Task. In this paper, we report descriptions of methodologies used for participation. We submit outputs from multi-encoder based transformer model, where one encoder is for context and another for source utterance. We consider one previous utterance as context. We obtain COMET scores of 0.768 and 0.907 on English-to-German and German-to-English directions, respectively. We submitted outputs without using context at all, which generated worse results in English-to-German direction. While for German-to-English, the model achieved a lower COMET score but slightly higher chrF and BLEU scores. Further, to understand the effectiveness of the context encoder, we submitted a run after removing the context encoder during testing and we obtain similar results.
Cross-modal Transfer Between Vision and Language for Protest Detection

Most of today’s systems for socio-political event detection are text-based, while an increasing amount of information published on the web is multi-modal. We seek to bridge this gap by proposing a method that utilizes existing annotated unimodal data to perform event detection in another data modality, zero-shot. Specifically, we focus on protest detection in text and images, and show that a pretrained vision-and-language alignment model (CLIP) can be leveraged towards this end. In particular, our results suggest that annotated protest text data can act supplementarily for detecting protests in images, but significant transfer is demonstrated in the opposite direction as well.
A Multifaceted Framework to Evaluate Evasion, Content Preservation, and Misattribution in Authorship Obfuscation Techniques

Authorship obfuscation techniques have commonly been evaluated based on their ability to hide the author’s identity (evasion) while preserving the content of the original text. However, to avoid overstating the systems’ effectiveness, evasion detection must be evaluated using competitive identification techniques in settings that mimic real-life scenarios, and the outcomes of the content-preservation evaluation have to be interpretable by potential users of these obfuscation tools. Motivated by recent work on cross-topic authorship identification and content preservation in summarization, we re-evaluate different authorship obfuscation techniques on detection evasion and content preservation. Furthermore, we propose a new information-theoretic measure to characterize the misattribution harm that can be caused by detection evasion. Our results reveal key weaknesses in state-of-the-art obfuscation techniques and a surprisingly competitive effectiveness from a back-translation baseline in all evaluation aspects.
HW-TSC Translation Systems for the WMT22 Chat Translation Task

This paper describes the submissions of Huawei Translation Services Center (HW-TSC) to WMT22 chat translation shared task on English-Germany (en-de) bidirection with results of zore-shot and few-shot tracks. We use the deep transformer architecture with a lager parameter size. Our submissions to the WMT21 News Translation task are used as the baselines. We adopt strategies such as back translation, forward translation, domain transfer, data selection, and noisy forward translation in task, and achieve competitive results on the development set. We also test the effectiveness of document translation on chat tasks. Due to the lack of chat data, the results on the development set show that it is not as effective as sentence-level translation models.
Break it Down into BTS: Basic, Tiniest Subword Units for Korean

We introduce Basic, Tiniest Subword (BTS) units for the Korean language, which are inspired by the invention principle of Hangeul, the Korean writing system. Instead of relying on 51 Korean consonant and vowel letters, we form the letters from BTS units by adding strokes or combining them. To examine the impact of BTS units on Korean language processing, we develop a novel BTS-based word embedding framework that is readily applicable to various models. Our experiments reveal that BTS units significantly improve the performance of Korean word embedding on all intrinsic and extrinsic tasks in our evaluation. In particular, BTS-based word embedding outperforms the state-of-theart Korean word embedding by 11.8% in word analogy. We further investigate the unique advantages provided by BTS units through indepth analysis.
RelCLIP: Adapting Language-Image Pretraining for Visual Relationship Detection via Relational Contrastive Learning

Conventional visual relationship detection models only use the numeric ids of relation labels for training, but ignore the semantic correlation between the labels, which leads to severe training biases and harms the generalization ability of representations. In this paper, we introduce compact language information of relation labels for regularizing the representation learning of visual relations. Specifically, we propose a simple yet effective visual Relationship prediction framework that transfers natural language knowledge learned from Contrastive Language-Image Pre-training (CLIP) models to enhance the relationship prediction, termed RelCLIP. Benefiting from the powerful visual-semantic alignment ability of CLIP at image level, we introduce a novel Relational Contrastive Learning (RCL) approach which explores relation-level visual-semantic alignment via learning to match cross-modal relational embeddings. By collaboratively learning the semantic coherence and discrepancy from relation triplets, the model can generate more discriminative and robust representations. Experimental results on the Visual Genome dataset show that RelCLIP achieves significant improvements over strong baselines under full (provide accurate labels) and distant supervision (provide noise labels), demonstrating its powerful generalization ability in learning relationship representations. Code will be available at https://gitee.com/mindspore/models/tree/master/research/cv/RelCLIP.
Angiotensin II increases respiratory rhythmic activity in the preBötzinger complex without inducing astroglial calcium signaling

Angiotensin II (Ang II) is the primary modulator of the renin-angiotensin system and has been widely studied for its effect on the cardiovascular system. While a few studies have also indicated an involvement of Ang II in the regulation of breathing, very little is known in this regard and its effect on brainstem respiratory regions such as the preBötzinger complex (preBötC), the kernel for inspiratory rhythm generation, has not been investigated yet. This study reports that Ang II temporarily increases phrenic nerve activity in the working heart-brainstem preparation, indicating higher central respiratory drive. Previous studies have shown that the carotid body is involved in mediating this effect and we revealed that the preBötC also plays a part, using acute slices of the brainstem. It appears that Ang II is increasing the respiratory drive in an AT1R-dependent manner by optimizing the interaction of inhibitory and excitatory neurons of the preBötC. Thus, Ang II-mediated effects on the preBötC are potentially involved in dysregulating breathing in patients with acute lung injury.
Magnetic ethylene-based periodic mesoporous organosilica supported palladium: An efficient and recoverable nanocatalyst for Suzuki reaction

In the present study, a novel magnetic ethylene-based periodic mesoporous organosilica supported Pd-Schiff base complex (Fe3O4@PMO/SB-Pd) was prepared, characterized and applied as a recoverable nanocatalyst for green synthesis of Suzuki products. Chemical composition, magnetic and thermal behavior, morphology and particle size of Fe3O4@PMO/SB-Pd were investigated by using FT-IR, TGA, EDX, VSM, PXRD, TEM and Scanning electron microscopy (SEM) analyses. The Fe3O4@PMO/SB-Pd nanocomposite was applied as an efficient nanocatalyst in the Suzuki reaction under ultrasonic conditions giving corresponding products in high yield. Some advantages of this study are simple purification of products, the use of water solvent, easy catalyst separation, short reaction time and high catalyst efficiency and recoverability.
The impact of cefuroxime prophylaxis on human intestinal microbiota in surgical oncological patients

Introduction The intestinal microbiota is vital to human health, and has a profound influence on several biological processes including inflammation and pathogen resistance. Antibiotic intake greatly impacts bacterial diversity, can increase antibiotic resistance and impair the equilibrium between bacterial species. The key to grasping post-antibiotic effects on the gut microbiota rests on the implementation of a suitable procedure to isolate microbial DNA and a meticulous consideration of experimental sequencing artefacts. Methods We herein report the bacterial community dynamics of a cohort of 128 surgical oncology patients before and after the intravenous administration of cefuroxime, an antibiotic routinely used in surgical antibioprophylaxis with proven efficiency against both gram-positive and gram-negative bacteria. In our study, we analyzed patient fecal samples collected through rectal examination before and 7 days post cefuroxime treatment by employing a high-throughput sequencing assay which targets the V3–V4 region of the 16S rRNA gene. A first challenge in applying the study design was to extract an appropriate amount of DNA characteristic to the sampled microbiota, which implied the use of both mechanical (ceramic beads) and chemical (proteinase K, lysozyme and lysostaphin) lysis. Results Gut microbiota richness and composition was significantly different between the two groups, but most differences were determined by additional perioperative procedures, rather than antibioprophylaxis. Intestinal microbiota composition was not significantly changed one week post cefuroxime treatment when compared to pre-treatment condition for patients without mechanical bowel preparation, but some loss in taxonomic variety could be observed. Discussion Taken together, cefuroxime does not promote short-term dysbiosis in surgical patients without any additional perioperative procedures.
Mortalin: Protein partners, biological impacts, pathological roles, and therapeutic opportunities

Mortalin (GRP75, HSPA9A), a heat shock protein (HSP), regulates a wide range of cellular processes, including cell survival, growth, and metabolism. The regulatory functions of mortalin are mediated through a diverse set of protein partners associated with different cellular compartments, which allows mortalin to perform critical functions under physiological conditions, including mitochondrial protein quality control. However, alteration of mortalin’s activities, its abnormal subcellular compartmentalization, and its protein partners turn mortalin into a disease-driving protein in different pathological conditions, including cancers. Here, mortalin’s contributions to tumorigenic pathways are explained. Pathology information based on mortalin’s RNA expression extracted from The Cancer Genome Atlas (TCGA) transcriptomic database indicates that mortalin has an independent prognostic value in common tumors, including lung, breast, and colorectal cancer (CRC). Subsequently, the binding partners of mortalin reported in different cellular models, from yeast to mammalian cells, and its regulation by post-translational modifications are discussed. Finally, we focus on colorectal cancer and discuss how mortalin and its tumorigenic downstream protein targets are regulated by a ubiquitin-like protein through the 26S proteasomal degradation machinery. A broader understanding of the function of mortalin and its positive and negative regulation in the formation and progression of human diseases, particularly cancer, is essential for developing new strategies to treat a diverse set of human diseases critically associated with dysregulated mortalin.