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Opinion summarization is the task of automatically generating summaries that encapsulate information expressed in multiple user reviews. In this work, we propose an LF-based bi-level optimization framework WISDOM to solve these two critical limitations. We show that a significant portion of errors in such systems arise from asking irrelevant or un-interpretable questions and that such errors can be ameliorated by providing summarized input.

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We also propose a multi-label malevolence detection model, multi-faceted label correlation enhanced CRF (MCRF), with two label correlation mechanisms, label correlation in taxonomy (LCT) and label correlation in context (LCC). Francesco Moramarco. Javier Rando Ramírez. This paper introduces QAConv, a new question answering (QA) dataset that uses conversations as a knowledge source. We use the D-cons generated by DoCoGen to augment a sentiment classifier and a multi-label intent classifier in 20 and 78 DA setups, respectively, where source-domain labeled data is scarce. Probing Multilingual Cognate Prediction Models. Language Correspondences | Language and Communication: Essential Concepts for User Interface and Documentation Design | Oxford Academic. But if we are able to accept that the uniformitarian model may not always be relevant, then we can tolerate a substantially revised time line. Keywords: English-Polish dictionary; linguistics; Polish-English glossary of terms. In this account the separation of peoples is caused by the great deluge, which carried people into different parts of the earth. BenchIE: A Framework for Multi-Faceted Fact-Based Open Information Extraction Evaluation.

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Transformer-based models are the modern work horses for neural machine translation (NMT), reaching state of the art across several benchmarks. On a new interactive flight–booking task with natural language, our model more accurately infers rewards and predicts optimal actions in unseen environments, in comparison to past work that first maps language to actions (instruction following) and then maps actions to rewards (inverse reinforcement learning). Angle of an issueFACET. What is false cognates in english. On the GLUE benchmark, UniPELT consistently achieves 1 4% gains compared to the best individual PELT method that it incorporates and even outperforms fine-tuning under different setups. Experiments on MS-MARCO, Natural Question, and Trivia QA datasets show that coCondenser removes the need for heavy data engineering such as augmentation, synthesis, or filtering, and the need for large batch training.

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In conclusion, our findings suggest that when evaluating automatic translation metrics, researchers should take data variance into account and be cautious to report the results on unreliable datasets, because it may leads to inconsistent results with most of the other datasets. Sanket Vaibhav Mehta. We conduct experiments on two text classification datasets – Jigsaw Toxicity, and Bias in Bios, and evaluate the correlations between metrics and manual annotations on whether the model produced a fair outcome. Using Cognates to Develop Comprehension in English. In this study, we crowdsource multiple-choice reading comprehension questions for passages taken from seven qualitatively distinct sources, analyzing what attributes of passages contribute to the difficulty and question types of the collected examples. Codes are available at Headed-Span-Based Projective Dependency Parsing.

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Such slang, in which a set phrase is used instead of the more standard expression with which it rhymes, as in "elephant's trunk" instead of "drunk" (, 94), has in London even "spread from the working-class East End to well-educated dwellers in suburbia, who practise it to exercise their brains just as they might eagerly try crossword puzzles" (, 97). We conduct extensive experiments and show that our CeMAT can achieve significant performance improvement for all scenarios from low- to extremely high-resource languages, i. e., up to +14. AdaLoGN: Adaptive Logic Graph Network for Reasoning-Based Machine Reading Comprehension. Linguistic term for a misleading cognate crossword december. Nevertheless, podcast summarization faces significant challenges including factual inconsistencies of summaries with respect to the inputs. To this end, models generally utilize an encoder-only (like BERT) paradigm or an encoder-decoder (like T5) approach. Although several studies in the past have highlighted the limitations of ROUGE, researchers have struggled to reach a consensus on a better alternative until today. We then discuss the importance of creating annotations for lower-resourced languages in a thoughtful and ethical way that includes the language speakers as part of the development process. Quality Estimation (QE) models have the potential to change how we evaluate and maybe even train machine translation models. For each question, we provide the corresponding KoPL program and SPARQL query, so that KQA Pro can serve for both KBQA and semantic parsing tasks. The experiments on ComplexWebQuestions and WebQuestionSP show that our method outperforms SOTA methods significantly, demonstrating the effectiveness of program transfer and our framework.

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Code is available at Exploring the Impact of Negative Samples of Contrastive Learning: A Case Study of Sentence Embedding. Despite the surge of new interpretation methods, it remains an open problem how to define and quantitatively measure the faithfulness of interpretations, i. e., to what extent interpretations reflect the reasoning process by a model. Then, we develop a novel probabilistic graphical framework GroupAnno to capture annotator group bias with an extended Expectation Maximization (EM) algorithm. We evaluate how much data is needed to obtain a query-by-example system that is usable by linguists. However, the computational patterns of FFNs are still unclear. Our experiments on language modeling, machine translation, and masked language model finetuning show that our approach outperforms previous efficient attention models; compared to the strong transformer baselines, it significantly improves the inference time and space efficiency with no or negligible accuracy loss. Notice that in verse four of the account they even seem to mention this intention: And they said, Go to, let us build us a city and a tower, whose top may reach unto heaven; and let us make us a name, lest we be scattered abroad upon the face of the whole earth. Dense retrieval has achieved impressive advances in first-stage retrieval from a large-scale document collection, which is built on bi-encoder architecture to produce single vector representation of query and document.

Leveraging Wikipedia article evolution for promotional tone detection. The fact that the fundamental issue in the Babel account involves dispersion (filling the earth or scattering) may also be illustrated by the chiastic structure of the account. Transformer based re-ranking models can achieve high search relevance through context- aware soft matching of query tokens with document tokens. The key idea in Transkimmer is to add a parameterized predictor before each layer that learns to make the skimming decision. In detail, we introduce an in-passage negative sampling strategy to encourage a diverse generation of sentence representations within the same passage. Previous methods commonly restrict the region (in feature space) of In-domain (IND) intent features to be compact or simply-connected implicitly, which assumes no OOD intents reside, to learn discriminative semantic features.

In this paper, we propose Dictionary Prior (DPrior), a new data-driven prior that enjoys the merits of expressivity and controllability. Existing studies focus on further optimizing by improving negative sampling strategy or extra pretraining. But would non-domesticated animals have done so as well? 0), and scientific commonsense (QASC) benchmarks. Via these experiments, we also discover an exception to the prevailing wisdom that "fine-tuning always improves performance". To endow the model with the ability of discriminating contradictory patterns, we minimize the similarity between the target response and contradiction related negative example. In particular, models are tasked with retrieving the correct image from a set of 10 minimally contrastive candidates based on a contextual such, each description contains only the details that help distinguish between cause of this, descriptions tend to be complex in terms of syntax and discourse and require drawing pragmatic inferences.

The proposed framework can be integrated into most existing SiMT methods to further improve performance. In this paper, we present a decomposed meta-learning approach which addresses the problem of few-shot NER by sequentially tackling few-shot span detection and few-shot entity typing using meta-learning. The key idea is based on the observation that if we traverse a constituency tree in post-order, i. e., visiting a parent after its children, then two consecutively visited spans would share a boundary. This paper presents the first Thai Nested Named Entity Recognition (N-NER) dataset. We train a contextual semantic parser using our strategy, and obtain 79% turn-by-turn exact match accuracy on the reannotated test set. Combining (Second-Order) Graph-Based and Headed-Span-Based Projective Dependency Parsing. Chryssi Giannitsarou. Prior research on radiology report summarization has focused on single-step end-to-end models – which subsume the task of salient content acquisition. Experimental results on three language pairs demonstrate that DEEP results in significant improvements over strong denoising auto-encoding baselines, with a gain of up to 1. Thus, the majority of the world's languages cannot benefit from recent progress in NLP as they have no or limited textual data. In this paper, we present the VHED (VIST Human Evaluation Data) dataset, which first re-purposes human evaluation results for automatic evaluation; hence we develop Vrank (VIST Ranker), a novel reference-free VIST metric for story evaluation. Karthik Krishnamurthy. Based on Bayesian inference we are able to effectively quantify uncertainty at prediction time.

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