King's username and password for access off campus. "Bin Laden had an Islamic frame of reference, but he didn't have anything against the Arab regimes, " Montasser al-Zayat, a lawyer for many of the Islamists, told me recently in Cairo. We adapt the previously proposed gradient reversal layer framework to encode two article versions simultaneously and thus leverage this additional training signal. However, most of them focus on the constitution of positive and negative representation pairs and pay little attention to the training objective like NT-Xent, which is not sufficient enough to acquire the discriminating power and is unable to model the partial order of semantics between sentences. As such, it can be applied to black-box pre-trained models without a need for architectural manipulations, reassembling of modules, or re-training. Previous works have employed many hand-crafted resources to bring knowledge-related into models, which is time-consuming and labor-intensive. For FGET, a key challenge is the low-resource problem — the complex entity type hierarchy makes it difficult to manually label data. Our method achieves the lowest expected calibration error compared to strong baselines on both in-domain and out-of-domain test samples while maintaining competitive accuracy. However, existing methods tend to provide human-unfriendly interpretation, and are prone to sub-optimal performance due to one-side promotion, i. either inference promotion with interpretation or vice versa. Rex Parker Does the NYT Crossword Puzzle: February 2020. El Moatez Billah Nagoudi. However, existing hyperbolic networks are not completely hyperbolic, as they encode features in the hyperbolic space yet formalize most of their operations in the tangent space (a Euclidean subspace) at the origin of the hyperbolic model.
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To address this bottleneck, we introduce the Belgian Statutory Article Retrieval Dataset (BSARD), which consists of 1, 100+ French native legal questions labeled by experienced jurists with relevant articles from a corpus of 22, 600+ Belgian law articles. Specifically, we propose a retrieval-augmented code completion framework, leveraging both lexical copying and referring to code with similar semantics by retrieval. To counter authorship attribution, researchers have proposed a variety of rule-based and learning-based text obfuscation approaches.
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Experimental results on VQA show that FewVLM with prompt-based learning outperforms Frozen which is 31x larger than FewVLM by 18. Our benchmarks cover four jurisdictions (European Council, USA, Switzerland, and China), five languages (English, German, French, Italian and Chinese) and fairness across five attributes (gender, age, region, language, and legal area). Sentence-level Privacy for Document Embeddings. Our main goal is to understand how humans organize information to craft complex answers. In this study, we propose an early stopping method that uses unlabeled samples. 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 addition, our model yields state-of-the-art results in terms of Mean Absolute Error. However, they have been shown vulnerable to adversarial attacks especially for logographic languages like Chinese. Predicting missing facts in a knowledge graph (KG) is crucial as modern KGs are far from complete. In an educated manner wsj crossword contest. We suggest a method to boost the performance of such models by adding an intermediate unsupervised classification task, between the pre-training and fine-tuning phases. Country Life Archive presents a chronicle of more than 100 years of British heritage, including its art, architecture, and landscapes, with an emphasis on leisure pursuits such as antique collecting, hunting, shooting, equestrian news, and gardening. Charts are commonly used for exploring data and communicating insights. And they became the leaders.
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We present coherence boosting, an inference procedure that increases a LM's focus on a long context. Domain Knowledge Transferring for Pre-trained Language Model via Calibrated Activation Boundary Distillation. In addition, our analysis unveils new insights, with detailed rationales provided by laypeople, e. g., that the commonsense capabilities have been improving with larger models while math capabilities have not, and that the choices of simple decoding hyperparameters can make remarkable differences on the perceived quality of machine text. In an educated manner wsj crossword answer. Toxic language detection systems often falsely flag text that contains minority group mentions as toxic, as those groups are often the targets of online hate. Our code is publicly available at Continual Sequence Generation with Adaptive Compositional Modules. Comprehensive evaluation on topic mining shows that UCTopic can extract coherent and diverse topical phrases. As such, improving its computational efficiency becomes paramount. First, we introduce a novel labeling strategy, which contains two sets of token pair labels, namely essential label set and whole label set. Such over-reliance on spurious correlations also causes systems to struggle with detecting implicitly toxic help mitigate these issues, we create ToxiGen, a new large-scale and machine-generated dataset of 274k toxic and benign statements about 13 minority groups. Our approach consists of 1) a method for training data generators to generate high-quality, label-consistent data samples; and 2) a filtering mechanism for removing data points that contribute to spurious correlations, measured in terms of z-statistics.
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However, some existing sparse methods usually use fixed patterns to select words, without considering similarities between words. Our model is experimentally validated on both word-level and sentence-level tasks. In an educated manner wsj crossword puzzles. In this study, based on the knowledge distillation framework and multi-task learning, we introduce the similarity metric model as an auxiliary task to improve the cross-lingual NER performance on the target domain. Pangrams: OUTGROWTH, WROUGHT. Our approach successfully quantifies measurable gaps between human authored text and generations from models of several sizes, including fourteen configurations of GPT-3.
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Then, we propose classwise extractive-then-abstractive/abstractive summarization approaches to this task, which can employ a modern transformer-based seq2seq network like BART and can be applied to various repositories without specific constraints. We show that DoCoGen can generate coherent counterfactuals consisting of multiple sentences. For Zawahiri, bin Laden was a savior—rich and generous, with nearly limitless resources, but also pliable and politically unformed. Each report presents detailed statistics alongside expert commentary and forecasting from the EIU's analysts. Hence, we introduce Neural Singing Voice Beautifier (NSVB), the first generative model to solve the SVB task, which adopts a conditional variational autoencoder as the backbone and learns the latent representations of vocal tone. The Zawahiri (pronounced za-wah-iri) clan was creating a medical dynasty. During each stage, we independently apply different continuous prompts for allowing pre-trained language models better shift to translation tasks. To address these issues, we propose a novel Dynamic Schema Graph Fusion Network (DSGFNet), which generates a dynamic schema graph to explicitly fuse the prior slot-domain membership relations and dialogue-aware dynamic slot relations. We also present a model that incorporates knowledge generated by COMET using soft positional encoding and masked show that both retrieved and COMET-generated knowledge improve the system's performance as measured by automatic metrics and also by human evaluation. A reduction of quadratic time and memory complexity to sublinear was achieved due to a robust trainable top-k experiments on a challenging long document summarization task show that even our simple baseline performs comparably to the current SOTA, and with trainable pooling we can retain its top quality, while being 1.
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We build on the work of Kummerfeld and Klein (2013) to propose a transformation-based framework for automating error analysis in document-level event and (N-ary) relation extraction. Automatic Error Analysis for Document-level Information Extraction. Even given a morphological analyzer, naive sequencing of morphemes into a standard BERT architecture is inefficient at capturing morphological compositionality and expressing word-relative syntactic regularities. Current methods achieve decent performance by utilizing supervised learning and large pre-trained language models. Additionally, SixT+ offers a set of model parameters that can be further fine-tuned to other unsupervised tasks. Recent work in deep fusion models via neural networks has led to substantial improvements over unimodal approaches in areas like speech recognition, emotion recognition and analysis, captioning and image description. We make all of the test sets and model predictions available to the research community at Large Scale Substitution-based Word Sense Induction. Second, the dataset supports question generation (QG) task in the education domain. Our method generalizes to new few-shot tasks and avoids catastrophic forgetting of previous tasks by enforcing extra constraints on the relational embeddings and by adding extra relevant data in a self-supervised manner. Modeling Temporal-Modal Entity Graph for Procedural Multimodal Machine Comprehension. End-to-end simultaneous speech-to-text translation aims to directly perform translation from streaming source speech to target text with high translation quality and low latency.
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To achieve this, our approach encodes small text chunks into independent representations, which are then materialized to approximate the shallow representation of BERT. We separately release the clue-answer pairs from these puzzles as an open-domain question answering dataset containing over half a million unique clue-answer pairs. FrugalScore: Learning Cheaper, Lighter and Faster Evaluation Metrics for Automatic Text Generation. ClarET: Pre-training a Correlation-Aware Context-To-Event Transformer for Event-Centric Generation and Classification. Inferring the members of these groups constitutes a challenging new NLP task: (i) Information is distributed over many poorly-constructed posts; (ii) Threats and threat agents are highly contextual, with the same post potentially having multiple agents assigned to membership in either group; (iii) An agent's identity is often implicit and transitive; and (iv) Phrases used to imply Outsider status often do not follow common negative sentiment patterns. When pre-trained contextualized embedding-based models developed for unstructured data are adapted for structured tabular data, they perform admirably. In this paper, we propose a neural model EPT-X (Expression-Pointer Transformer with Explanations), which utilizes natural language explanations to solve an algebraic word problem.
Down and Across: Introducing Crossword-Solving as a New NLP Benchmark. To this day, everyone has or (more likely) will enjoy a crossword at some point in their life, but not many people know the variations of crosswords and how they differentiate. Extensive experiments demonstrate our method achieves state-of-the-art results in both automatic and human evaluation, and can generate informative text and high-resolution image responses. Decisions on state-level policies have a deep effect on many aspects of our everyday life, such as health-care and education access. A few large, homogenous, pre-trained models undergird many machine learning systems — and often, these models contain harmful stereotypes learned from the internet.
Our code and checkpoints will be available at Understanding Multimodal Procedural Knowledge by Sequencing Multimodal Instructional Manuals. Based on this analysis, we propose a new approach to human evaluation and identify several challenges that must be overcome to develop effective biomedical MDS systems. Extensive experiments on both Chinese and English songs demonstrate the effectiveness of our methods in terms of both objective and subjective metrics. To evaluate the performance of the proposed model, we construct two new datasets based on the Reddit comments dump and Twitter corpus.