Multitasking Framework for Unsupervised Simple Definition Generation. Analyses further discover that CNM is capable of learning model-agnostic task taxonomy. The core codes are contained in Appendix E. Lexical Knowledge Internalization for Neural Dialog Generation. Our results show that our models can predict bragging with macro F1 up to 72. Therefore, in this paper, we design an efficient Transformer architecture, named Fourier Sparse Attention for Transformer (FSAT), for fast long-range sequence modeling. In an educated manner crossword clue. Prediction Difference Regularization against Perturbation for Neural Machine Translation. Experimental results show that outperforms state-of-the-art baselines which utilize word-level or sentence-level representations.
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Meanwhile, we apply a prediction consistency regularizer across the perturbed models to control the variance due to the model diversity. Then we systematically compare these different strategies across multiple tasks and domains. Experiments on a wide range of few shot NLP tasks demonstrate that Perfect, while being simple and efficient, also outperforms existing state-of-the-art few-shot learning methods. It is AI's Turn to Ask Humans a Question: Question-Answer Pair Generation for Children's Story Books. In this paper, we aim to address the overfitting problem and improve pruning performance via progressive knowledge distillation with error-bound properties. Synthesizing QA pairs with a question generator (QG) on the target domain has become a popular approach for domain adaptation of question answering (QA) models. In an educated manner wsj crossword key. Sense embedding learning methods learn different embeddings for the different senses of an ambiguous word. Our extensive experiments suggest that contextual representations in PLMs do encode metaphorical knowledge, and mostly in their middle layers. We propose a novel posterior alignment technique that is truly online in its execution and superior in terms of alignment error rates compared to existing methods. Are Prompt-based Models Clueless? On the one hand, AdSPT adopts separate soft prompts instead of hard templates to learn different vectors for different domains, thus alleviating the domain discrepancy of the \operatorname{[MASK]} token in the masked language modeling task.
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We introduce a different but related task called positive reframing in which we neutralize a negative point of view and generate a more positive perspective for the author without contradicting the original meaning. Our main conclusion is that the contribution of constituent order and word co-occurrence is limited, while the composition is more crucial to the success of cross-linguistic transfer. We show that T5 models fail to generalize to unseen MRs, and we propose a template-based input representation that considerably improves the model's generalization capability. In an educated manner. Inducing Positive Perspectives with Text Reframing. Extensive experiments on two knowledge-based visual QA and two knowledge-based textual QA demonstrate the effectiveness of our method, especially for multi-hop reasoning problem. We find that search-query based access of the internet in conversation provides superior performance compared to existing approaches that either use no augmentation or FAISS-based retrieval (Lewis et al., 2020b).
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In this paper, we introduce ELECTRA-style tasks to cross-lingual language model pre-training. With extensive experiments on 6 multi-document summarization datasets from 3 different domains on zero-shot, few-shot and full-supervised settings, PRIMERA outperforms current state-of-the-art dataset-specific and pre-trained models on most of these settings with large margins. In an educated manner wsj crosswords. 2M example sentences in 8 English-centric language pairs. We further organize RoTs with a set of 9 moral and social attributes and benchmark performance for attribute classification.
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In this paper, we study two issues of semantic parsing approaches to conversational question answering over a large-scale knowledge base: (1) The actions defined in grammar are not sufficient to handle uncertain reasoning common in real-world scenarios. Such models are typically bottlenecked by the paucity of training data due to the required laborious annotation efforts. However, their attention mechanism comes with a quadratic complexity in sequence lengths, making the computational overhead prohibitive, especially for long sequences. In this work, we introduce a new fine-tuning method with both these desirable properties. However, the imbalanced training dataset leads to poor performance on rare senses and zero-shot senses. The code and data are available at Accelerating Code Search with Deep Hashing and Code Classification. In this paper, we find that the spreadsheet formula, a commonly used language to perform computations on numerical values in spreadsheets, is a valuable supervision for numerical reasoning in tables. We provide a brand-new perspective for constructing sparse attention matrix, i. e. making the sparse attention matrix predictable. We also perform extensive ablation studies to support in-depth analyses of each component in our framework. This results in improved zero-shot transfer from related HRLs to LRLs without reducing HRL representation and accuracy. Specifically, we formulate the novelty scores by comparing each application with millions of prior arts using a hybrid of efficient filters and a neural bi-encoder. In an educated manner wsj crossword solver. TSQA features a timestamp estimation module to infer the unwritten timestamp from the question.
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In particular, we show that well-known pathologies such as a high number of beam search errors, the inadequacy of the mode, and the drop in system performance with large beam sizes apply to tasks with high level of ambiguity such as MT but not to less uncertain tasks such as GEC. Overcoming Catastrophic Forgetting beyond Continual Learning: Balanced Training for Neural Machine Translation. Instead of computing the likelihood of the label given the input (referred as direct models), channel models compute the conditional probability of the input given the label, and are thereby required to explain every word in the input. We hope that these techniques can be used as a starting point for human writers, to aid in reducing the complexity inherent in the creation of long-form, factual text. Maria Leonor Pacheco. Extensive experiments on both the public multilingual DBPedia KG and newly-created industrial multilingual E-commerce KG empirically demonstrate the effectiveness of SS-AGA. 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.
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0, a dataset labeled entirely according to the new formalism. Prodromos Malakasiotis. The FIBER dataset and our code are available at KenMeSH: Knowledge-enhanced End-to-end Biomedical Text Labelling. The dominant paradigm for high-performance models in novel NLP tasks today is direct specialization for the task via training from scratch or fine-tuning large pre-trained models. This work connects language model adaptation with concepts of machine learning theory. StableMoE: Stable Routing Strategy for Mixture of Experts.
Experiments show that UIE achieved the state-of-the-art performance on 4 IE tasks, 13 datasets, and on all supervised, low-resource, and few-shot settings for a wide range of entity, relation, event and sentiment extraction tasks and their unification. Finally, we document other attempts that failed to yield empirical gains, and discuss future directions for the adoption of class-based LMs on a larger scale. Results show that our model achieves state-of-the-art performance on most tasks and analysis reveals that comment and AST can both enhance UniXcoder. Experimental results on a benckmark dataset show that our method is highly effective, leading a 2. Created Feb 26, 2011. Current open-domain conversational models can easily be made to talk in inadequate ways.
Our insistence on meaning preservation makes positive reframing a challenging and semantically rich task. Ion Androutsopoulos. Modeling Hierarchical Syntax Structure with Triplet Position for Source Code Summarization. Experimental results show that our proposed CBBGCA training framework significantly improves the NMT model by +1. The relabeled dataset is released at, to serve as a more reliable test set of document RE models. Using three publicly-available datasets, we show that finetuning a toxicity classifier on our data improves its performance on human-written data substantially. We apply these metrics to better understand the commonly-used MRPC dataset and study how it differs from PAWS, another paraphrase identification dataset. Rik Koncel-Kedziorski. We train our model on a diverse set of languages to learn a parameter initialization that can adapt quickly to new languages. Language-agnostic BERT Sentence Embedding. A cascade of tasks are required to automatically generate an abstractive summary of the typical information-rich radiology report. Because we are not aware of any appropriate existing datasets or attendant models, we introduce a labeled dataset (CT5K) and design a model (NP2IO) to address this task.
Recent studies have shown the advantages of evaluating NLG systems using pairwise comparisons as opposed to direct assessment. In this paper, we introduce the time-segmented evaluation methodology, which is novel to the code summarization research community, and compare it with the mixed-project and cross-project methodologies that have been commonly used. 3) Do the findings for our first question change if the languages used for pretraining are all related? While Contrastive-Probe pushes the acc@10 to 28%, the performance gap still remains notable. Learning Disentangled Textual Representations via Statistical Measures of Similarity.
Specifically, we devise a three-stage training framework to incorporate the large-scale in-domain chat translation data into training by adding a second pre-training stage between the original pre-training and fine-tuning stages. Saving and revitalizing endangered languages has become very important for maintaining the cultural diversity on our planet. Most previous methods for text data augmentation are limited to simple tasks and weak baselines. The Zawahiri (pronounced za-wah-iri) clan was creating a medical dynasty. "The Zawahiris are professors and scientists, and they hate to speak of politics, " he said. In our work, we utilize the oLMpics bench- mark and psycholinguistic probing datasets for a diverse set of 29 models including T5, BART, and ALBERT. Specifically, we propose CeMAT, a conditional masked language model pre-trained on large-scale bilingual and monolingual corpora in many languages. Our approach first reduces the dimension of token representations by encoding them using a novel autoencoder architecture that uses the document's textual content in both the encoding and decoding phases.
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I'b) From which crop does Farmer Elias. A city block is 3 hm long and 2 hm wide. Discuss the questions in Exercise 1. with the class after they have spent. Division by Decimals • Time and Temperature. E., Brackets first; Multiplication, [Division, Addition, Subtraction in the. The polyhedra shown. Allow the less able and average. Consisting of two segments with a. common endpoint (as illustrated). 6, (a) N X 9 ==■■ 63. The sum of sharon's and john's ages is 70 and 50. Numeration systems, 59. This manner: L M, L N.. and L P? Emphasize that all the.
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