Broader disclosive transparency---truth and clarity in communication regarding the function of AI systems---is widely considered desirable. Unfortunately, it is a nebulous concept, difficult to both define and quantify. This is problematic, as previo
us work has demonstrated possible trade-offs and negative consequences to disclosive transparency, such as a confusion effect, where too much information'' clouds a reader's understanding of what a system description means. Disclosive transparency's subjective nature has rendered deep study into these problems and their remedies difficult. To improve this state of affairs, We introduce neural language model-based probabilistic metrics to directly model disclosive transparency, and demonstrate that they correlate with user and expert opinions of system transparency, making them a valid objective proxy. Finally, we demonstrate the use of these metrics in a pilot study quantifying the relationships between transparency, confusion, and user perceptions in a corpus of real NLP system descriptions.
Being able to accurately perform Question Difficulty Estimation (QDE) can improve the accuracy of students' assessment and better their learning experience. Traditional approaches to QDE are either subjective or introduce a long delay before new ques
tions can be used to assess students. Thus, recent work proposed machine learning-based approaches to overcome these limitations. They use questions of known difficulty to train models capable of inferring the difficulty of questions from their text. Once trained, they can be used to perform QDE of newly created questions. Existing approaches employ supervised models which are domain-dependent and require a large dataset of questions of known difficulty for training. Therefore, they cannot be used if such a dataset is not available ( for new courses on an e-learning platform). In this work, we experiment with the possibility of performing QDE from text in an unsupervised manner. Specifically, we use the uncertainty of calibrated question answering models as a proxy of human-perceived difficulty. Our experiments show promising results, suggesting that model uncertainty could be successfully leveraged to perform QDE from text, reducing both costs and elapsed time.
Research in Natural Language Processing is making rapid advances, resulting in the publication of a large number of research papers. Finding relevant research papers and their contribution to the domain is a challenging problem. In this paper, we add
ress this challenge via the SemEval 2021 Task 11: NLPContributionGraph, by developing a system for a research paper contributions-focused knowledge graph over Natural Language Processing literature. The task is divided into three sub-tasks: extracting contribution sentences that show important contributions in the research article, extracting phrases from the contribution sentences, and predicting the information units in the research article together with triplet formation from the phrases. The proposed system is agnostic to the subject domain and can be applied for building a knowledge graph for any area. We found that transformer-based language models can significantly improve existing techniques and utilized the SciBERT-based model. Our first sub-task uses Bidirectional LSTM (BiLSTM) stacked on top of SciBERT model layers, while the second sub-task uses Conditional Random Field (CRF) on top of SciBERT with BiLSTM. The third sub-task uses a combined SciBERT based neural approach with heuristics for information unit prediction and triplet formation from the phrases. Our system achieved F1 score of 0.38, 0.63 and 0.76 in end-to-end pipeline testing, phrase extraction testing and triplet extraction testing respectively.
This paper describes our systems for negation detection and time expression recognition in SemEval 2021 Task 10, Source-Free Domain Adaptation for Semantic Processing. We show that self-training, active learning and data augmentation techniques can i
mprove the generalization ability of the model on the unlabeled target domain data without accessing source domain data. We also perform detailed ablation studies and error analyses for our time expression recognition systems to identify the source of the performance improvement and give constructive feedback on the temporal normalization annotation guidelines.
The introduction of pre-trained transformer-based contextualized word embeddings has led to considerable improvements in the accuracy of graph-based parsers for frameworks such as Universal Dependencies (UD). However, previous works differ in various
dimensions, including their choice of pre-trained language models and whether they use LSTM layers. With the aims of disentangling the effects of these choices and identifying a simple yet widely applicable architecture, we introduce STEPS, a new modular graph-based dependency parser. Using STEPS, we perform a series of analyses on the UD corpora of a diverse set of languages. We find that the choice of pre-trained embeddings has by far the greatest impact on parser performance and identify XLM-R as a robust choice across the languages in our study. Adding LSTM layers provides no benefits when using transformer-based embeddings. A multi-task training setup outputting additional UD features may contort results. Taking these insights together, we propose a simple but widely applicable parser architecture and configuration, achieving new state-of-the-art results (in terms of LAS) for 10 out of 12 diverse languages.
Time-offset interaction applications (TOIA) allow simulating conversations with people who have previously recorded relevant video utterances, which are played in response to their interacting user. TOIAs have great potential for preserving cross-gen
erational and cross-cultural histories, online teaching, simulated interviews, etc. Current TOIAs exist in niche contexts involving high production costs. Democratizing TOIA presents different challenges when creating appropriate pre-recordings, designing different user stories, and creating simple online interfaces for experimentation. We open-source TOIA 2.0, a user-centered time-offset interaction application, and make it available for everyone who wants to interact with people's pre-recordings, or create their pre-recordings.
In this paper, we propose an implementation of temporal semantics that translates syntax trees to logical formulas, suitable for consumption by the Coq proof assistant. The analysis supports a wide range of phenomena including: temporal references, t
emporal adverbs, aspectual classes and progressives. The new semantics are built on top of a previous system handling all sections of the FraCaS test suite except the temporal reference section, and we obtain an accuracy of 81 percent overall and 73 percent for the problems explicitly marked as related to temporal reference. To the best of our knowledge, this is the best performance of a logical system on the whole of the FraCaS.
يعرف القانون الدولي الإنساني بأنه مجموعة القواعد القانونية التي تهدف لتسوية المشكلات المترتبة مباشرة على النزاعات المسلحة الدولية أو غير الدولية, و التي تقيد لأسباب إنسانية حق أطراف النزاع في استخدام سبل و وسائل الحرب محل اختيارها و التي تحمي الممتلك
ات و الأشخاص المتضررين أو المحتمل تضررهم من النزاع.
In this paper defined important expressions, a
remembered important theorem which we need , approved
essential theorem to be exist non trivial Holomorphically
projective mapping between Kahlerian spaces.
Finally we specified Kahlerian spaces which have
maximum degree of variance parabolically – Kahlerian
spaces.
In this paper we study conformal mappings between
special Parabolically Kahlerian Spaces (commutative spaces).
A proved , if exist conformal mapping between commutative
Kahlerin spaces ,then the mapping is Homothetic
mapping,
