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Document-level neural machine translation (DocNMT) achieves coherent translations by incorporating cross-sentence context. Their analysis, which is at the center of legal practice, becomes increasingly elaborate as these collections grow in size. We compare the methods with respect to their ability to reduce the partial input bias while maintaining the overall performance. Generalized zero-shot text classification aims to classify textual instances from both previously seen classes and incrementally emerging unseen classes. Moreover, we trained predictive models to detect argumentative discourse structures and embedded them in an adaptive writing support system for students that provides them with individual argumentation feedback independent of an instructor, time, and location. Can Pre-trained Language Models Interpret Similes as Smart as Human? We then design a harder self-supervision objective by increasing the ratio of negative samples within a contrastive learning setup, and enhance the model further through automatic hard negative mining coupled with a large global negative queue encoded by a momentum encoder. In an educated manner. We then pretrain the LM with two joint self-supervised objectives: masked language modeling and our new proposal, document relation prediction. Interpreting Character Embeddings With Perceptual Representations: The Case of Shape, Sound, and Color.

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In this paper, we collect a dataset of realistic aspect-oriented summaries, AspectNews, which covers different subtopics about articles in news sub-domains. We release two parallel corpora which can be used for the training of detoxification models. In this work, we propose a method to train a Functional Distributional Semantics model with grounded visual data. In an educated manner wsj crossword crossword puzzle. Sanket Vaibhav Mehta. Then we design a popularity-oriented and a novelty-oriented module to perceive useful signals and further assist final prediction. Data augmentation with RGF counterfactuals improves performance on out-of-domain and challenging evaluation sets over and above existing methods, in both the reading comprehension and open-domain QA settings. To determine the importance of each token representation, we train a Contribution Predictor for each layer using a gradient-based saliency method.

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Harnessing linguistically diverse conversational corpora will provide the empirical foundations for flexible, localizable, humane language technologies of the future. In an educated manner wsj crossword answer. The first, Ayman and a twin sister, Umnya, were born on June 19, 1951. In this paper, we identify that the key issue is efficient contrastive learning. In this paper, we propose a unified text-to-structure generation framework, namely UIE, which can universally model different IE tasks, adaptively generate targeted structures, and collaboratively learn general IE abilities from different knowledge sources. Rather, we design structure-guided code transformation algorithms to generate synthetic code clones and inject real-world security bugs, augmenting the collected datasets in a targeted way.

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While a great deal of work has been done on NLP approaches to lexical semantic change detection, other aspects of language change have received less attention from the NLP community. We show that the metric can be theoretically linked with a specific notion of group fairness (statistical parity) and individual fairness. Daniel Preotiuc-Pietro. The Zawahiri (pronounced za-wah-iri) clan was creating a medical dynasty. Typically, prompt-based tuning wraps the input text into a cloze question. The learning trajectories of linguistic phenomena in humans provide insight into linguistic representation, beyond what can be gleaned from inspecting the behavior of an adult speaker. Rex Parker Does the NYT Crossword Puzzle: February 2020. While our proposed objectives are generic for encoders, to better capture spreadsheet table layouts and structures, FORTAP is built upon TUTA, the first transformer-based method for spreadsheet table pretraining with tree attention. Information extraction suffers from its varying targets, heterogeneous structures, and demand-specific schemas. In text-to-table, given a text, one creates a table or several tables expressing the main content of the text, while the model is learned from text-table pair data. When training data from multiple languages are available, we also integrate MELM with code-mixing for further improvement.

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Finally, we design an effective refining strategy on EMC-GCN for word-pair representation refinement, which considers the implicit results of aspect and opinion extraction when determining whether word pairs match or not. In the theoretical portion of this paper, we take the position that the goal of probing ought to be measuring the amount of inductive bias that the representations encode on a specific task. However, these methods require the training of a deep neural network with several parameter updates for each update of the representation model. Pretrained multilingual models enable zero-shot learning even for unseen languages, and that performance can be further improved via adaptation prior to finetuning. Ablation studies demonstrate the importance of local, global, and history information. The provided empirical evidences show that CsaNMT sets a new level of performance among existing augmentation techniques, improving on the state-of-the-art by a large margin. In an educated manner wsj crossword clue. 25 in the top layer, while the self-similarity of GPT-2 sentence embeddings formed using the EOS token increases layer-over-layer and never falls below. However, this can be very expensive as the number of human annotations required would grow quadratically with k. In this work, we introduce Active Evaluation, a framework to efficiently identify the top-ranked system by actively choosing system pairs for comparison using dueling bandit algorithms. "He was a mysterious character, closed and introverted, " Zaki Mohamed Zaki, a Cairo journalist who was a classmate of his, told me. Back-translation is a critical component of Unsupervised Neural Machine Translation (UNMT), which generates pseudo parallel data from target monolingual data.

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We show that despite the differences among datasets and annotations, robust cross-domain classification is possible. Automatic and human evaluations on the Oxford dictionary dataset show that our model can generate suitable examples for targeted words with specific definitions while meeting the desired readability. This work explores techniques to predict Part-of-Speech (PoS) tags from neural signals measured at millisecond resolution with electroencephalography (EEG) during text reading. This assumption may lead to performance degradation during inference, where the model needs to compare several system-generated (candidate) summaries that have deviated from the reference summary. But real users' needs often fall in between these extremes and correspond to aspects, high-level topics discussed among similar types of documents. Although recently proposed trainable conversation-level metrics have shown encouraging results, the quality of the metrics is strongly dependent on the quality of training data. Our evaluations showed that TableFormer outperforms strong baselines in all settings on SQA, WTQ and TabFact table reasoning datasets, and achieves state-of-the-art performance on SQA, especially when facing answer-invariant row and column order perturbations (6% improvement over the best baseline), because previous SOTA models' performance drops by 4% - 6% when facing such perturbations while TableFormer is not affected. We propose a spatial commonsense benchmark that focuses on the relative scales of objects, and the positional relationship between people and objects under different probe PLMs and models with visual signals, including vision-language pretrained models and image synthesis models, on this benchmark, and find that image synthesis models are more capable of learning accurate and consistent spatial knowledge than other models.

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We show how fine-tuning on this dataset results in conversations that human raters deem considerably more likely to lead to a civil conversation, without sacrificing engagingness or general conversational ability. Do self-supervised speech models develop human-like perception biases? Multi-hop question generation focuses on generating complex questions that require reasoning over multiple pieces of information of the input passage. Particularly, we first propose a multi-task pre-training strategy to leverage rich unlabeled data along with external labeled data for representation learning. At inference time, instead of the standard Gaussian distribution used by VAE, CUC-VAE allows sampling from an utterance-specific prior distribution conditioned on cross-utterance information, which allows the prosody features generated by the TTS system to be related to the context and is more similar to how humans naturally produce prosody. EGT2 learns the local entailment relations by recognizing the textual entailment between template sentences formed by typed CCG-parsed predicates. Goals in this environment take the form of character-based quests, consisting of personas and motivations. George Michalopoulos. 8% of the performance, runs 24 times faster, and has 35 times less parameters than the original metrics. We also devise a layerwise distillation strategy to transfer knowledge from unpruned to pruned models during optimization.

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However, these approaches only utilize a single molecular language for representation learning. We perform extensive experiments with 13 dueling bandits algorithms on 13 NLG evaluation datasets spanning 5 tasks and show that the number of human annotations can be reduced by 80%. For 19 under-represented languages across 3 tasks, our methods lead to consistent improvements of up to 5 and 15 points with and without extra monolingual text respectively. We analyze different strategies to synthesize textual or labeled data using lexicons, and how this data can be combined with monolingual or parallel text when available. We then formulate the next-token probability by mixing the previous dependency modeling probability distributions with self-attention. A follow-up probing analysis indicates that its success in the transfer is related to the amount of encoded contextual information and what is transferred is the knowledge of position-aware context dependence of results provide insights into how neural network encoders process human languages and the source of cross-lingual transferability of recent multilingual language models. Providing more readable but inaccurate versions of texts may in many cases be worse than providing no such access at all. With a base PEGASUS, we push ROUGE scores by 5. The ability to sequence unordered events is evidence of comprehension and reasoning about real world tasks/procedures. Simile interpretation is a crucial task in natural language processing. One sense of an ambiguous word might be socially biased while its other senses remain unbiased. Pigeon perch crossword clue. Probing Simile Knowledge from Pre-trained Language Models. 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.

Linguistically diverse conversational corpora are an important and largely untapped resource for computational linguistics and language technology. In particular, we propose a neighborhood-oriented packing strategy, which considers the neighbor spans integrally to better model the entity boundary information. Further, we show that popular datasets potentially favor models biased towards easy cues which are available independent of the context. Experiment results show that BiTiIMT performs significantly better and faster than state-of-the-art LCD-based IMT on three translation tasks. I need to look up examples, hang on... huh... weird... when I google [funk rap] the very first hit I get is for G-FUNK, which I *have* heard of. However, in low resource settings, validation-based stopping can be risky because a small validation set may not be sufficiently representative, and the reduction in the number of samples by validation split may result in insufficient samples for training. The essential label set consists of the basic labels for this task, which are relatively balanced and applied in the prediction layer. 92 F1) and strong performance on CTB (92. Comprehensive experiments for these applications lead to several interesting results, such as evaluation using just 5% instances (selected via ILDAE) achieves as high as 0. To fill this gap, we ask the following research questions: (1) How does the number of pretraining languages influence zero-shot performance on unseen target languages? A consortium of Egyptian Jewish financiers, intending to create a kind of English village amid the mango and guava plantations and Bedouin settlements on the eastern bank of the Nile, began selling lots in the first decade of the twentieth century. 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.

Our code and models are publicly available at An Interpretable Neuro-Symbolic Reasoning Framework for Task-Oriented Dialogue Generation. Existing studies on CLS mainly focus on utilizing pipeline methods or jointly training an end-to-end model through an auxiliary MT or MS objective. Besides, the generalization ability matters a lot in nested NER, as a large proportion of entities in the test set hardly appear in the training set. Besides formalizing the approach, this study reports simulations of human experiments with DIORA (Drozdov et al., 2020), a neural unsupervised constituency parser. Taxonomy (Zamir et al., 2018) finds that a structure exists among visual tasks, as a principle underlying transfer learning for them. On the other hand, logic-based approaches provide interpretable rules to infer the target answer, but mostly work on structured data where entities and relations are well-defined.

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