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To implement the approach, we utilize RELAX (Grathwohl et al., 2018), a contemporary gradient estimator which is both low-variance and unbiased, and we fine-tune the baseline in a few-shot style for both stability and computational efficiency. Our analysis with automatic and human evaluation shows that while our best models usually generate fluent summaries and yield reasonable BLEU scores, they also suffer from hallucinations and factual errors as well as difficulties in correctly explaining complex patterns and trends in charts. We release a corpus of crossword puzzles collected from the New York Times daily crossword spanning 25 years and comprised of a total of around nine thousand puzzles. Rex Parker Does the NYT Crossword Puzzle: February 2020. Finally, to enhance the robustness of QR systems to questions of varying hardness, we propose a novel learning framework for QR that first trains a QR model independently on each subset of questions of a certain level of hardness, then combines these QR models as one joint model for inference.

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To this end, we present CONTaiNER, a novel contrastive learning technique that optimizes the inter-token distribution distance for Few-Shot NER. He was a bookworm and hated contact sports—he thought they were "inhumane, " according to his uncle Mahfouz. In an educated manner wsj crossword puzzle answers. Previous work of class-incremental learning for Named Entity Recognition (NER) relies on the assumption that there exists abundance of labeled data for the training of new classes. It is a unique archive of analysis and explanation of political, economic and commercial developments, together with historical statistical data. We have created detailed guidelines for capturing moments of change and a corpus of 500 manually annotated user timelines (18. Our proposed model can generate reasonable examples for targeted words, even for polysemous words.

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Although a multilingual version of the T5 model (mT5) was also introduced, it is not clear how well it can fare on non-English tasks involving diverse data. We employ a model explainability tool to explore the features that characterize hedges in peer-tutoring conversations, and we identify some novel features, and the benefits of a such a hybrid model approach. Experiments show that our method can significantly improve the translation performance of pre-trained language models. Bodhisattwa Prasad Majumder. To address this challenge, we propose the CQG, which is a simple and effective controlled framework. This paper aims to distill these large models into smaller ones for faster inference and with minimal performance loss. The social impact of natural language processing and its applications has received increasing attention. In an educated manner crossword clue. Local models for Entity Disambiguation (ED) have today become extremely powerful, in most part thanks to the advent of large pre-trained language models.

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Empirical results show that our proposed methods are effective under the new criteria and overcome limitations of gradient-based methods on removal-based criteria. In an educated manner wsj crossword solutions. Imputing Out-of-Vocabulary Embeddings with LOVE Makes LanguageModels Robust with Little Cost. Language-agnostic BERT Sentence Embedding. In addition to being more principled and efficient than round-trip MT, our approach offers an adjustable parameter to control the fidelity-diversity trade-off, and obtains better results in our experiments.

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Improving Compositional Generalization with Self-Training for Data-to-Text Generation. Surprisingly, both of them use multilingual masked language model (MLM) without any cross-lingual supervision or aligned data. The center of this cosmopolitan community was the Maadi Sporting Club. We point out that existing learning-to-route MoE methods suffer from the routing fluctuation issue, i. e., the target expert of the same input may change along with training, but only one expert will be activated for the input during inference. We use IMPLI to evaluate NLI models based on RoBERTa fine-tuned on the widely used MNLI dataset. Specifically, it first retrieves turn-level utterances of dialogue history and evaluates their relevance to the slot from a combination of three perspectives: (1) its explicit connection to the slot name; (2) its relevance to the current turn dialogue; (3) Implicit Mention Oriented Reasoning. In an educated manner wsj crossword giant. Word identification from continuous input is typically viewed as a segmentation task. Bias Mitigation in Machine Translation Quality Estimation. Lastly, we apply our metrics to filter the output of a paraphrase generation model and show how it can be used to generate specific forms of paraphrases for data augmentation or robustness testing of NLP models. "They condemned me for making what they called a 'coup d'état. ' Experimental results show that our paradigm outperforms other methods that use weakly-labeled data and improves a state-of-the-art baseline by 4. In this paper, we explore strategies for finding the similarity between new users and existing ones and methods for using the data from existing users who are a good match. Comprehensive evaluation on topic mining shows that UCTopic can extract coherent and diverse topical phrases. To answer this currently open question, we introduce the Legal General Language Understanding Evaluation (LexGLUE) benchmark, a collection of datasets for evaluating model performance across a diverse set of legal NLU tasks in a standardized way.

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However, there has been relatively less work on analyzing their ability to generate structured outputs such as graphs. Experimental results show that our method achieves general improvements on all three benchmarks (+0. Auto-Debias: Debiasing Masked Language Models with Automated Biased Prompts. Each summary is written by the researchers who generated the data and associated with a scientific paper. Lexically constrained neural machine translation (NMT), which controls the generation of NMT models with pre-specified constraints, is important in many practical scenarios. Images are often more significant than only the pixels to human eyes, as we can infer, associate, and reason with contextual information from other sources to establish a more complete picture. To mitigate such limitations, we propose an extension based on prototypical networks that improves performance in low-resource named entity recognition tasks. Building on the Prompt Tuning approach of Lester et al. Moreover, we report a set of benchmarking results, and the results indicate that there is ample room for improvement. Label semantic aware systems have leveraged this information for improved text classification performance during fine-tuning and prediction. The relabeled dataset is released at, to serve as a more reliable test set of document RE models.

To address these issues, we propose to answer open-domain multi-answer questions with a recall-then-verify framework, which separates the reasoning process of each answer so that we can make better use of retrieved evidence while also leveraging large models under the same memory constraint. Our experiments, done on a large public dataset of ASL fingerspelling in the wild, show the importance of fingerspelling detection as a component of a search and retrieval model. Our full pipeline improves the performance of state-of-the-art models by a relative 50% in F1-score.

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