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Answering complex questions that require multi-hop reasoning under weak supervision is considered as a challenging problem since i) no supervision is given to the reasoning process and ii) high-order semantics of multi-hop knowledge facts need to be captured. This technique combines easily with existing approaches to data augmentation, and yields particularly strong results in low-resource settings. "Ayman told me that his love of medicine was probably inherited. In an educated manner crossword clue. 3% in average score of a machine-translated GLUE benchmark. We examine the representational spaces of three kinds of state of the art self-supervised models: wav2vec, HuBERT and contrastive predictive coding (CPC), and compare them with the perceptual spaces of French-speaking and English-speaking human listeners, both globally and taking account of the behavioural differences between the two language groups. Hence, we expect VALSE to serve as an important benchmark to measure future progress of pretrained V&L models from a linguistic perspective, complementing the canonical task-centred V&L evaluations.

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In this paper, we study the named entity recognition (NER) problem under distant supervision. Based on this dataset, we study two novel tasks: generating textual summary from a genomics data matrix and vice versa. Attack vigorously crossword clue. In an educated manner wsj crossword daily. Our code is available at Github. In comparison to other widely used strategies for selecting important tokens, such as saliency and attention, our proposed method has a significantly lower false positive rate in generating rationales. He had also served at various times as the Egyptian ambassador to Pakistan, Yemen, and Saudi Arabia. To test this hypothesis, we formulate a set of novel fragmentary text completion tasks, and compare the behavior of three direct-specialization models against a new model we introduce, GibbsComplete, which composes two basic computational motifs central to contemporary models: masked and autoregressive word prediction. In linguistics, there are two main perspectives on negation: a semantic and a pragmatic view.

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On the other hand, the discrepancies between Seq2Seq pretraining and NMT finetuning limit the translation quality (i. e., domain discrepancy) and induce the over-estimation issue (i. e., objective discrepancy). In this paper, we propose a deep-learning based inductive logic reasoning method that firstly extracts query-related (candidate-related) information, and then conducts logic reasoning among the filtered information by inducing feasible rules that entail the target relation. Little attention has been paid to UE in natural language processing. Deep learning (DL) techniques involving fine-tuning large numbers of model parameters have delivered impressive performance on the task of discriminating between language produced by cognitively healthy individuals, and those with Alzheimer's disease (AD). One limitation of NAR-TTS models is that they ignore the correlation in time and frequency domains while generating speech mel-spectrograms, and thus cause blurry and over-smoothed results. "When Ayman met bin Laden, he created a revolution inside him. In an educated manner wsj crossword december. We also link to ARGEN datasets through our repository: Legal Judgment Prediction via Event Extraction with Constraints. FaiRR: Faithful and Robust Deductive Reasoning over Natural Language. Intuitively, if the chatbot can foresee in advance what the user would talk about (i. e., the dialogue future) after receiving its response, it could possibly provide a more informative response. Our model is divided into three independent components: extracting direct-speech, compiling a list of characters, and attributing those characters to their utterances.

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Grammar, vocabulary, and lexical semantic shifts take place over time, resulting in a diachronic linguistic gap. In conjunction with language agnostic meta learning, this enables us to fine-tune a high-quality text-to-speech model on just 30 minutes of data in a previously unseen language spoken by a previously unseen speaker. Text-based games provide an interactive way to study natural language processing. More specifically, we probe their capabilities of storing the grammatical structure of linguistic data and the structure learned over objects in visual data. Rex Parker Does the NYT Crossword Puzzle: February 2020. Nonetheless, these approaches suffer from the memorization overfitting issue, where the model tends to memorize the meta-training tasks while ignoring support sets when adapting to new tasks. Experiments on a large-scale WMT multilingual dataset demonstrate that our approach significantly improves quality on English-to-Many, Many-to-English and zero-shot translation tasks (from +0. Our code is available at Reducing Position Bias in Simultaneous Machine Translation with Length-Aware Framework. We view fake news detection as reasoning over the relations between sources, articles they publish, and engaging users on social media in a graph framework.

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This reduces the number of human annotations required further by 89%. On average over all learned metrics, tasks, and variants, FrugalScore retains 96. In this paper, we investigate injecting non-local features into the training process of a local span-based parser, by predicting constituent n-gram non-local patterns and ensuring consistency between non-local patterns and local constituents. Finally, we analyze the informativeness of task-specific subspaces in contextual embeddings as well as which benefits a full parser's non-linear parametrization provides. Moreover, with this paper, we suggest stopping focusing on improving performance under unreliable evaluation systems and starting efforts on reducing the impact of proposed logic traps. Intrinsic evaluations of OIE systems are carried out either manually—with human evaluators judging the correctness of extractions—or automatically, on standardized benchmarks. In an educated manner wsj crossword contest. In this work, we introduce a family of regularizers for learning disentangled representations that do not require training. Fantastically Ordered Prompts and Where to Find Them: Overcoming Few-Shot Prompt Order Sensitivity.

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Based on it, we further uncover and disentangle the connections between various data properties and model performance. To address this problem, previous works have proposed some methods of fine-tuning a large model that pretrained on large-scale datasets. 2M example sentences in 8 English-centric language pairs. Experiments show that our approach brings models best robustness improvement against ATP, while also substantially boost model robustness against NL-side perturbations. Thirdly, it should be robust enough to handle various surface forms of the generated sentence. To further facilitate the evaluation of pinyin input method, we create a dataset consisting of 270K instances from fifteen sults show that our approach improves the performance on abbreviated pinyin across all analysis demonstrates that both strategiescontribute to the performance boost.

Nevertheless, podcast summarization faces significant challenges including factual inconsistencies of summaries with respect to the inputs. The source code is publicly released at "You might think about slightly revising the title": Identifying Hedges in Peer-tutoring Interactions. With the increasing popularity of posting multimodal messages online, many recent studies have been carried out utilizing both textual and visual information for multi-modal sarcasm detection. In this work, we introduce solving crossword puzzles as a new natural language understanding task. Prevailing methods transfer the knowledge derived from mono-granularity language units (e. g., token-level or sample-level), which is not enough to represent the rich semantics of a text and may lose some vital knowledge. The recently proposed Fusion-in-Decoder (FiD) framework is a representative example, which is built on top of a dense passage retriever and a generative reader, achieving the state-of-the-art performance. South Asia is home to a plethora of languages, many of which severely lack access to new language technologies. This hybrid method greatly limits the modeling ability of networks. To this end, we formulate the Distantly Supervised NER (DS-NER) problem via Multi-class Positive and Unlabeled (MPU) learning and propose a theoretically and practically novel CONFidence-based MPU (Conf-MPU) approach. To facilitate research in this direction, we collect real-world biomedical data and present the first Chinese Biomedical Language Understanding Evaluation (CBLUE) benchmark: a collection of natural language understanding tasks including named entity recognition, information extraction, clinical diagnosis normalization, single-sentence/sentence-pair classification, and an associated online platform for model evaluation, comparison, and analysis. GLM improves blank filling pretraining by adding 2D positional encodings and allowing an arbitrary order to predict spans, which results in performance gains over BERT and T5 on NLU tasks. By building speech synthesis systems for three Indigenous languages spoken in Canada, Kanien'kéha, Gitksan & SENĆOŦEN, we re-evaluate the question of how much data is required to build low-resource speech synthesis systems featuring state-of-the-art neural models.

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