Reviewerly/exHarmony huggingface dataset:
Zero-shot Neural Passage Retrieval via Domain-targeted Synthetic  Question Generation

A major obstacle to the wide-spread adoption of neural retrieval models is that they require large supervised training sets to surpass traditional term-based techniques, which are constructed from raw corpora. In this paper, we propose an approach to zero-shot learning for passage retrieval that uses synthetic question generation to close this gap. The question generation system is trained on general domain data, but is applied to documents in the targeted domain. This allows us to create arbitrarily large, yet noisy, question-passage relevance pairs that are domain specific. Furthermore, when this is coupled with a simple hybrid term-neural model, first-stage retrieval performance can be improved further. Empirically, we show that this is an effective strategy for building neural passage retrieval models in the absence of large training corpora. Depending on the domain, this technique can even approach the accuracy of supervised models.
On the proof complexity of logics of bounded branching

We investigate the proof complexity of extended Frege (EF) systems for basic transitive modal logics (K4, S4, GL, …) augmented with the bounded branching axioms BBk. First, we study feasibility of the disjunction property and more general extension rules in EF systems for these logics: we show that the corresponding decision problems reduce to total coNP search problems (or equivalently, disjoint NP pairs, in the binary case); more precisely, the decision problem for extension rules is equivalent to a certain special case of interpolation for the classical EF system. Next, we use this characterization to prove superpolynomial (or even exponential, with stronger hypotheses) separations between EF and substitution Frege (SF) systems for all transitive logics contained in S4.2GrzBB2 or GL.2BB2 under some assumptions weaker than PSPACE≠NP. We also prove analogous results for superintuitionistic logics: we characterize the decision complexity of multi-conclusion Visser's rules in EF systems for Gabbay–de Jongh logics Tk, and we show conditional separations between EF and SF for all intermediate logics contained in T2+KC.
Graph Sequential Network for Reasoning over Sequences

Recently Graph Neural Network (GNN) has been applied successfully to various NLP tasks that require reasoning, such as multi-hop machine reading comprehension. In this paper, we consider a novel case where reasoning is needed over graphs built from sequences, i.e. graph nodes with sequence data. Existing GNN models fulfill this goal by first summarizing the node sequences into fixed-dimensional vectors, then applying GNN on these vectors. To avoid information loss inherent in the early summarization and make sequential labeling tasks on GNN output feasible, we propose a new type of GNN called Graph Sequential Network (GSN), which features a new message passing algorithm based on co-attention between a node and each of its neighbors. We validate the proposed GSN on two NLP tasks: interpretable multi-hop reading comprehension on HotpotQA and graph based fact verification on FEVER. Both tasks require reasoning over multiple documents or sentences. Our experimental results show that the proposed GSN attains better performance than the standard GNN based methods.
Grammar Accuracy Evaluation (GAE): Quantifiable Qualitative Evaluation of Machine Translation Models

Natural Language Generation (NLG) refers to the operation of expressing the calculation results of a system in human language. Since the quality of generated sentences from an NLG model cannot be fully represented using only quantitative evaluation, they are evaluated using qualitative evaluation by humans in which the meaning or grammar of a sentence is scored according to a subjective criterion. Nevertheless, the existing evaluation methods have a problem as a large score deviation occurs depending on the criteria of evaluators. In this paper, we propose Grammar Accuracy Evaluation (GAE) that can provide the specific evaluating criteria. As a result of analyzing the quality of machine translation by BLEU and GAE, it was confirmed that the BLEU score does not represent the absolute performance of machine translation models and GAE compensates for the shortcomings of BLEU with flexible evaluation of alternative synonyms and changes in sentence structure.
THE FORMATION OF FUTURE TEACHERS PROFESSIONAL READYNESS TO INNOVATION ACTIVITY BY MEANS OF DIGITAL TECHNOLOGIES

&#x0D; The training of future specialists for innovation activity is a leading trend of modern higher pedagogical education, which is due to the integration of Ukraine into the European educational space. The article actualizes the problem of future teachers’ professional readiness formation for innovation activity using digital technologies. General science, synthesis, comparison, analysis, generalization and systematization of studies of domestic and foreign scientists are employed. Based on the results, the essence of the concept “professional preparedness of future teachers for innovation” has been revealed. It has been found that the professional readiness of future teachers for innovation is the outcome of integrative personal education, and professional training, featured via a blend of cognitive (knowledge regarding the concept of the innovation and profession), motivational (incentives for interests and innovation), reflexive (capability of analyzing results), innovative activity and praxeological (innovation competence actualization) elements that permit conducting professional tasks of innovative nature. It has been concluded that a modern teacher can’t conduct educational actions with no utilizing advanced digital technologies, and the education informatization comprises enhancing the future teachers’ training in the information educational environment of higher education institutions.
Image Enhancement Technology in Pavement Disease Detection System

Efficient pavement bad location detection and repair is essential to prolong the use time of roads. However, traditional manual detection methods are extremely inefficient and can no longer meet the requirements of inspecting a large number of roads. When using deep learning technology for road disease detection, it is found that low-illuminance images will affect the detection accuracy due to low contrast. Therefore, before training and testing the deep learning model, the original image needs to be preprocessed to improve the image quality. First, bilateral filtering is used instead of Gaussian filtering to estimate the illuminance of the original image; Then the reflection component is get according to the principle of Retinex algorithm, and the reflection image is quantized; Finally, the image is subjected to illumination compensation. The results of comparative experiments display that the ours algorithm can retain the characteristic details of road diseases and eliminate the unevenness of the image brightness distribution while improving the contrast of the road image.
DS4DH at SemEval-2022 Task 11: Multilingual Named Entity Recognition Using an Ensemble of Transformer-based Language Models

In this paper, we describe our proposed method for the SemEval 2022 Task 11: Multilingual Complex Named Entity Recognition (Multi-CoNER).The goal of this task is to locate and classify named entities in unstructured short complex texts in 11 different languages.After training a variety of contextual language models on the NER dataset, we used an ensemble strategy based on a majority vote to finalize our model.We evaluated our proposed approach on the multilingual NER dataset at SemEval-2022.The ensemble model provided consistent improvements against the individual models on the multilingual track, achieving a macro F1 performance of 65.2%.However, our results were significantly outperformed by the top ranking systems, achieving thus a baseline performance.
UTNLP at SemEval-2022 Task 6: A Comparative Analysis of Sarcasm Detection using generative-based and mutation-based data augmentation

Sarcasm is a term that refers to the use of words to mock, irritate, or amuse someone. It is commonly used on social media. The metaphorical and creative nature of sarcasm presents a significant difficulty for sentiment analysis systems based on affective computing. The methodology and results of our team, UTNLP, in the SemEval-2022 shared task 6 on sarcasm detection are presented in this paper. We put different models, and data augmentation approaches to the test and report on which one works best. The tests begin with traditional machine learning models and progress to transformer-based and attention-based models. We employed data augmentation based on data mutation and data generation. Using RoBERTa and mutation-based data augmentation, our best approach achieved an F1-sarcastic of 0.38 in the competition's evaluation phase. After the competition, we fixed our model's flaws and achieved an F1-sarcastic of 0.414.
UAEM-ITAM at SemEval-2022 Task 5: Vision-Language Approach to Recognize Misogynous Content in Memes

In the context of the Multimedia Automatic Misogyny Identification (MAMI) competition 2022, we developed a framework for extracting lexical-semantic features from text and combine them with semantic descriptions of images, together with image content representation.We enriched the text modality description by incorporating word representations for each object present within the images.Images and text are then described at two levels of detail, globally and locally, using standard dimensionality reduction techniques for images in order to obtain 4 embeddings for each meme.These embeddings are finally concatenated and passed to a classifier.Our results overcome the baseline by 4%, falling behind the best performance by 12% for Sub-task B.
Optimization of Graph Total Variation via Active-Set-based Combinatorial  Reconditioning

Structured convex optimization on weighted graphs finds numerous applications in machine learning and computer vision. In this work, we propose a novel adaptive preconditioning strategy for proximal algorithms on this problem class. Our preconditioner is driven by a sharp analysis of the local linear convergence rate depending on the "active set" at the current iterate. We show that nested-forest decomposition of the inactive edges yields a guaranteed local linear convergence rate. Further, we propose a practical greedy heuristic which realizes such nested decompositions and show in several numerical experiments that our reconditioning strategy, when applied to proximal gradient or primal-dual hybrid gradient algorithm, achieves competitive performances. Our results suggest that local convergence analysis can serve as a guideline for selecting variable metrics in proximal algorithms.
Embarrassingly Simple Performance Prediction for Abductive Natural Language Inference

The task of abductive natural language inference (αNLI), to decide which hypothesis is the more likely explanation for a set of observations, is a particularly difficult type of NLI.Instead of just determining a causal relationship, it requires common sense to also evaluate how reasonable an explanation is.All recent competitive systems build on top of contextualized representations and make use of transformer architectures for learning an NLI model.When somebody is faced with a particular NLI task, they need to select the best model that is available.This is a time-consuming and resource-intense endeavour.To solve this practical problem, we propose a simple method for predicting the performance without actually fine-tuning the model.We do this by testing how well the pre-trained models perform on the αNLI task when just comparing sentence embeddings with cosine similarity to what the performance that is achieved when training a classifier on top of these embeddings.We show that the accuracy of the cosine similarity approach correlates strongly with the accuracy of the classification approach with a Pearson correlation coefficient of 0.65.Since the similarity computation is orders of magnitude faster to compute on a given dataset (less than a minute vs. hours), our method can lead to significant time savings in the process of model selection.
Knowledge Extraction From Texts Based on Wikidata

This paper presents an effort within our company of developing knowledge extraction pipeline for English, which can be further used for constructing an entreprise-specific knowledge base.We present a system consisting of entity detection and linking, coreference resolution, and relation extraction based on the Wikidata schema.We highlight existing challenges of knowledge extraction by evaluating the deployed pipeline on real-world data.We also make available a database, which can serve as a new resource for sentential relation extraction, and we underline the importance of having balanced data for training classification models 1 .
Revisiting Queer Minorities in Lexicons

This paper contains words that are offensive.Lexicons play an important role in content moderation, often being the first line of defense.However, little or no literature exists in analyzing the representation of queer-related words in them.In this paper, we consider twelve wellknown English lexicons containing inappropriate words and analyze how gender and sexual minorities are represented in these lexicons.Our analyses reveal that several of these lexicons barely make any distinction between pejorative and non-pejorative queer-related words.We express concern that such unfettered usage of non-pejorative queer-related words may impact queer presence in mainstream discourse.Our analyses further reveal that the lexicons have poor overlap in queer-related words.We finally present a quantifiable measure of consistency and show that several of these lexicons are not consistent in how they include (or omit) queer-related words.
Asynchronous Convergence in Multi-Task Learning via Knowledge Distillation from Converged Tasks

Multi-task learning (MTL) aims to solve multiple tasks jointly by sharing a base representation among them.This can lead to more efficient learning and better generalization, as compared to learning each task individually.However, one issue that often arises in MTL is the convergence speed between tasks varies due to differences in task difficulty, so it can be a challenge to simultaneously achieve the best performance on all tasks with a single model checkpoint.Various techniques have been proposed to address discrepancies in task convergence rate, including weighting the per-task losses and modifying task gradients.In this work, we propose a novel approach that avoids the problem of requiring all tasks to converge at the same rate, but rather allows for "asynchronous" convergence among the tasks where each task can converge on its own schedule.As our main contribution, we monitor per-task validation metrics and switch to a knowledge distillation loss once a task has converged instead of continuing to train on the true labels.This prevents the model from overfitting on converged tasks while it learns the remaining tasks.We evaluate the proposed method in two 5-task MTL setups consisting of internal ecommerce datasets.The results show that our method consistently outperforms existing loss weighting and gradient balancing approaches, achieving average improvements of 0.9% and 1.5% over the best performing baseline model in the two setups, respectively.
Automatic Exploration of Textual Environments with Language-Conditioned Autotelic Agents

The purpose of this extended abstract is to discuss the possible fruitful interactions between intrinsically-motivated language-conditioned agents and textual environments. We define autotelic agents as agents able to set their own goals. We identify desirable properties of textual nenvironments that makes them a good testbed for autotelic agents. We them list drivers of exploration for such agents that would allow them to achieve large repertoires of skills in these environments, enabling such agents to be repurposed for solving the benchmarks implemented in textual environments. We then discuss challenges and further perspectives brought about by this interaction.
SemEval-2022 Task 8: Multi-lingual News Article Similarity

This work is about finding the similarity between a pair of news articles.There are seven different objective similarity metrics provided in the dataset for each pair and the news articles are in multiple different languages.On top of the pre-trained embedding model, we calculated cosine similarity for baseline results and feed-forward neural network was then trained on top of it to improve the results.We also built separate pipelines for each similarity metric for feature extraction.We could see significant improvement from baseline results using feature extraction and feed-forward neural network.
DynamicTOC: Persona-based Table of Contents for Consumption of Long Documents

Long documents like contracts, financial documents, etc., are often tedious to read through.Linearly consuming (via scrolling or navigation through default table of content) these documents is time-consuming and challenging.These documents are also authored to be consumed by varied entities (referred to as persona in the paper) interested in only certain parts of the document.In this work, we describe DYNAMICTOC, a dynamic table of contentbased navigator, to aid in the task of non-linear, persona-based document consumption.DY-NAMICTOC highlights sections of interest in the document as per the aspects relevant to different personas.DYNAMICTOC is augmented with short questions to assist the users in understanding underlying content.This uses a novel deep-reinforcement learning technique to generate questions on these persona-clustered paragraphs.Human and automatic evaluations suggest the efficacy of both end-to-end pipeline and different components of DYNAMICTOC.
JARVix at SemEval-2022 Task 2: It Takes One to Know One? Idiomaticity Detection using Zero and One-Shot Learning

Large Language Models have been successful in a wide variety of Natural Language Processing tasks by capturing the compositionality of the text representations.In spite of their great success, these vector representations fail to capture meaning of idiomatic multi-word expressions (MWEs).In this paper, we focus on the detection of idiomatic expressions by using binary classification, based on Subtask A of SemEval-2022 Task 2 (Tayyar Madabushi et al., 2022).Thereafter, we perform the classification in two different settings: zero-shot and one-shot, to determine if a given sentence contains an idiom or not.N shot classification for this task is defined by N number of common idioms between the training and testing sets.In this paper, we train multiple Large Language Models in both the settings and achieve an F1 score (macro) of 0.73 for the zero-shot setting and an F1 score (macro) of 0.85 for the one-shot setting.An implementation of our work can be found at https://github.com/ashwinpathak20/Idiomaticity_Detection_Using_Few_ Shot_Learning.
Do Trajectories Encode Verb Meaning?

Distributional models learn representations of words from text, but are criticized for their lack of grounding, or the linking of text to the nonlinguistic world.Grounded language models have had success in learning to connect concrete categories like nouns and adjectives to the world via images and videos, but can struggle to isolate the meaning of the verbs themselves from the context in which they typically occur.In this paper, we investigate the extent to which trajectories (i.e. the position and rotation of objects over time) naturally encode verb semantics.We build a procedurally generated agent-object-interaction dataset, obtain human annotations for the verbs that occur in this data, and compare several methods for representation learning given the trajectories.We find that trajectories correlate as-is with some verbs (e.g., fall), and that additional abstraction via self-supervised pretraining can further capture nuanced differences in verb meaning (e.g., roll vs. slide).
RUG-1-Pegasussers at SemEval-2022 Task 3: Data Generation Methods to Improve Recognizing Appropriate Taxonomic Word Relations

This paper describes our system created for the SemEval 2022 Task 3: Presupposed Taxonomies -Evaluating Neural-network Semantics.This task is focused on correctly recognizing taxonomic word relations in English, French and Italian.We develop various data generation techniques that expand the originally provided train set and show that all methods increase the performance of models trained on these expanded datasets.Our final system outperforms the baseline from the task organizers by achieving an average macro F1 score of 79.6 on all languages, compared to the baseline's 67.4.
A Simple and Real-Time Support System for Firefighters Using Low-Cost 3-DOF Accelerometer and CO Sensor

During the operations, firefighters can be injured or killed because of the smoke and heat emission from the fire area, broken structure elements such as floors, walls, or boiling liquid ejection and gas explosion. Therefore, this paper aims to develop an efficient and portable system to monitor falls and high CO level through integrating a three degrees of freedom accelerometer and an MQ7 sensor to recorded acceleration and measured CO concentration with the embedded fall and high CO detection algorithms. The embedded fall detection algorithm can detect fall events with ultra-high accuracy without mistakenly identifying normal activities such as walking, standing, jogging, and jumping as fall events. The posture recognition and cascade posture recognition after three seconds are proposed in this paper to gain the accuracy of our proposed fall detection system. If a firefighter falls and is unable to stand up, the alert signal message will be sent to their commander outside through the GSM/GPRS module. The embedded high CO detection algorithm used to alert the dangerous CO level to recommend using self-contained breathing apparatuses (SCBA) and saving fresh air with acceptable CO level. We carefully investigated the proposed thresholds and window size before embedding them into the microcontroller. The sensitivity and accuracy achieved were around 96.5% and 93% respectively in our recorded data. Furthermore, the proposed fall detection algorithm also achieved higher geometric mean in comparison with Support Vector Machine classifier (SVM) and a nearest neighbor rule (NN) in the public datasets with the achieved around 99.44%, 98.41% and 95.76% respectively.
MS-PriGAN: prior knowledge-based multi-scale gradient generation adversarial network for ovarian cancer CT image generation

Despite the great success of deep neural networks in medical lesion detection tasks, their adaptation to CT images of ovarian cancer is known to be difficult, and the most important reason is the lack of ovarian cancer datasets. A commonly accepted reason for this deficiency is that few people are willing to disclose CT imaging data due to privacy concerns, despite the fact that ovarian cancer is the first most prevalent cancer among women. In this work, we propose a method for generating synthetic high-resolution medical images using generative adversarial networks. The method addresses the instability of previous work in generating work on small-scale datasets by a simple and effective technique to acquire prior knowledge and achieve multi-scale gradient flow. We show that the proposed method still achieves good results even when synthesised on small-scale data, and with FID scores better than the current top-performing networks (GANs).