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King's College members can refer to the official database documentation or this best practices guide for technical support and data integration guidance. ToxiGen: A Large-Scale Machine-Generated Dataset for Adversarial and Implicit Hate Speech Detection. In this paper, we propose a model that captures both global and local multimodal information for investment and risk management-related forecasting tasks. In an educated manner wsj crossword solver. First, a sketch parser translates the question into a high-level program sketch, which is the composition of functions. Dialogue systems are usually categorized into two types, open-domain and task-oriented. Structural Characterization for Dialogue Disentanglement. In this work we remedy both aspects.

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Entity alignment (EA) aims to discover the equivalent entity pairs between KGs, which is a crucial step for integrating multi-source a long time, most researchers have regarded EA as a pure graph representation learning task and focused on improving graph encoders while paying little attention to the decoding this paper, we propose an effective and efficient EA Decoding Algorithm via Third-order Tensor Isomorphism (DATTI). In such a low-resource setting, we devise a novel conversational agent, Divter, in order to isolate parameters that depend on multimodal dialogues from the entire generation model. First, we conduct a set of in-domain and cross-domain experiments involving three datasets (two from Argument Mining, one from the Social Sciences), modeling architectures, training setups and fine-tuning options tailored to the involved domains. We show that SAM is able to boost performance on SuperGLUE, GLUE, Web Questions, Natural Questions, Trivia QA, and TyDiQA, with particularly large gains when training data for these tasks is limited. A Meta-framework for Spatiotemporal Quantity Extraction from Text. To be specific, the final model pays imbalanced attention to training samples, where recently exposed samples attract more attention than earlier samples. 4 on static pictures, compared with 90. In an educated manner crossword clue. Since deriving reasoning chains requires multi-hop reasoning for task-oriented dialogues, existing neuro-symbolic approaches would induce error propagation due to the one-phase design.

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When pre-trained contextualized embedding-based models developed for unstructured data are adapted for structured tabular data, they perform admirably. 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. Adapting Coreference Resolution Models through Active Learning. MultiHiertt is built from a wealth of financial reports and has the following unique characteristics: 1) each document contain multiple tables and longer unstructured texts; 2) most of tables contained are hierarchical; 3) the reasoning process required for each question is more complex and challenging than existing benchmarks; and 4) fine-grained annotations of reasoning processes and supporting facts are provided to reveal complex numerical reasoning. In this work, we introduce a new task named Multimodal Chat Translation (MCT), aiming to generate more accurate translations with the help of the associated dialogue history and visual context. Vision-language navigation (VLN) is a challenging task due to its large searching space in the environment. Complex question answering over knowledge base (Complex KBQA) is challenging because it requires various compositional reasoning capabilities, such as multi-hop inference, attribute comparison, set operation, etc. Signal in Noise: Exploring Meaning Encoded in Random Character Sequences with Character-Aware Language Models. In addition, we perform knowledge distillation with a trained ensemble to generate new synthetic training datasets, "Troy-Blogs" and "Troy-1BW". We propose a benchmark to measure whether a language model is truthful in generating answers to questions. In an educated manner wsj crossword. To alleviate this trade-off, we propose an encoder-decoder architecture that enables intermediate text prompts at arbitrary time steps. Furthermore, our conclusions also echo that we need to rethink the criteria for identifying better pretrained language models. Then we design a popularity-oriented and a novelty-oriented module to perceive useful signals and further assist final prediction.

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Umayma Azzam, Rabie's wife, was from a clan that was equally distinguished but wealthier and also a little notorious. Recent work has explored using counterfactually-augmented data (CAD)—data generated by minimally perturbing examples to flip the ground-truth label—to identify robust features that are invariant under distribution shift. It is the most widely spoken dialect of Cree and a morphologically complex language that is polysynthetic, highly inflective, and agglutinative. Rex Parker Does the NYT Crossword Puzzle: February 2020. However, it is challenging to encode it efficiently into the modern Transformer architecture. Considering that most of current black-box attacks rely on iterative search mechanisms to optimize their adversarial perturbations, SHIELD confuses the attackers by automatically utilizing different weighted ensembles of predictors depending on the input.

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We design an automated question-answer generation (QAG) system for this education scenario: given a story book at the kindergarten to eighth-grade level as input, our system can automatically generate QA pairs that are capable of testing a variety of dimensions of a student's comprehension skills. We find that errors often appear in both that are not captured by existing evaluation metrics, motivating a need for research into ensuring the factual accuracy of automated simplification models. 1 BLEU points on the WMT14 English-German and German-English datasets, respectively. Experiments on MuST-C speech translation benchmark and further analysis show that our method effectively alleviates the cross-modal representation discrepancy, and achieves significant improvements over a strong baseline on eight translation directions. In particular, we experiment on Dependency Minimal Recursion Semantics (DMRS) and adapt PSHRG as a formalism that approximates the semantic composition of DMRS graphs and simultaneously recovers the derivations that license the DMRS graphs. Due to the incompleteness of the external dictionaries and/or knowledge bases, such distantly annotated training data usually suffer from a high false negative rate. CQG: A Simple and Effective Controlled Generation Framework for Multi-hop Question Generation. In this paper, we present a novel data augmentation paradigm termed Continuous Semantic Augmentation (CsaNMT), which augments each training instance with an adjacency semantic region that could cover adequate variants of literal expression under the same meaning. In an educated manner wsj crossword puzzle. Existing methods mainly focus on modeling the bilingual dialogue characteristics (e. g., coherence) to improve chat translation via multi-task learning on small-scale chat translation data. 23% showing that there is substantial room for improvement. In this work, we propose Masked Entity Language Modeling (MELM) as a novel data augmentation framework for low-resource NER. The Colonial State Papers offers access to over 7, 000 hand-written documents and more than 40, 000 bibliographic records with this incredible resource on Colonial History. Other Clues from Today's Puzzle.

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We compare uncertainty sampling strategies and their advantages through thorough error analysis. Currently, Medical Subject Headings (MeSH) are manually assigned to every biomedical article published and subsequently recorded in the PubMed database to facilitate retrieving relevant information. A Closer Look at How Fine-tuning Changes BERT. We release our pretrained models, LinkBERT and BioLinkBERT, as well as code and data. These findings suggest that there is some mutual inductive bias that underlies these models' learning of linguistic phenomena. Experimental results on the benchmark dataset demonstrate the effectiveness of our method and reveal the benefits of fine-grained emotion understanding as well as mixed-up strategy modeling. Experiments show that our method can significantly improve the translation performance of pre-trained language models. In this work, we propose Perfect, a simple and efficient method for few-shot fine-tuning of PLMs without relying on any such handcrafting, which is highly effective given as few as 32 data points.

Our mixture-of-experts SummaReranker learns to select a better candidate and consistently improves the performance of the base model. We study a new problem setting of information extraction (IE), referred to as text-to-table. However, despite their real-world deployment, we do not yet comprehensively understand the extent to which offensive language classifiers are robust against adversarial attacks. To tackle these issues, we propose a novel self-supervised adaptive graph alignment (SS-AGA) method. Unfortunately, existing prompt engineering methods require significant amounts of labeled data, access to model parameters, or both. However, the source words in the front positions are always illusoryly considered more important since they appear in more prefixes, resulting in position bias, which makes the model pay more attention on the front source positions in testing.
Educational Question Generation of Children Storybooks via Question Type Distribution Learning and Event-centric Summarization. With the rapid growth in language processing applications, fairness has emerged as an important consideration in data-driven solutions. At both the sentence- and the task-level, intrinsic uncertainty has major implications for various aspects of search such as the inductive biases in beam search and the complexity of exact search. Experiments show that the proposed method significantly outperforms strong baselines on multiple MMT datasets, especially when the textual context is limited. Besides text classification, we also apply interpretation methods and metrics to dependency parsing. As a matter of fact, the resulting nested optimization loop is both times consuming, adding complexity to the optimization dynamic, and requires a fine hyperparameter selection (e. g., learning rates, architecture). Learning Disentangled Representations of Negation and Uncertainty. By making use of a continuous-space attention mechanism to attend over the long-term memory, the ∞-former's attention complexity becomes independent of the context length, trading off memory length with order to control where precision is more important, ∞-former maintains "sticky memories, " being able to model arbitrarily long contexts while keeping the computation budget fixed. Search for award-winning films including Academy®, Emmy®, and Peabody® winners and access content from PBS, BBC, 60 MINUTES, National Geographic, Annenberg Learner, BroadwayHD™, A+E Networks' HISTORY® and more. Finding Structural Knowledge in Multimodal-BERT. We hypothesize that human performance is better characterized by flexible inference through composition of basic computational motifs available to the human language user. We propose two new criteria, sensitivity and stability, that provide complementary notions of faithfulness to the existed removal-based criteria. Turning Tables: Generating Examples from Semi-structured Tables for Endowing Language Models with Reasoning Skills. Second, we additionally break down the extractive part into two independent tasks: extraction of salient (1) sentences and (2) keywords.

Towards building intelligent dialogue agents, there has been a growing interest in introducing explicit personas in generation models. KQA Pro: A Dataset with Explicit Compositional Programs for Complex Question Answering over Knowledge Base. Our approach involves: (i) introducing a novel mix-up embedding strategy to the target word's embedding through linearly interpolating the pair of the target input embedding and the average embedding of its probable synonyms; (ii) considering the similarity of the sentence-definition embeddings of the target word and its proposed candidates; and, (iii) calculating the effect of each substitution on the semantics of the sentence through a fine-tuned sentence similarity model. Inferring Rewards from Language in Context. To address this problem, we devise DiCoS-DST to dynamically select the relevant dialogue contents corresponding to each slot for state updating. Experiments demonstrate that the proposed model outperforms the current state-of-the-art models on zero-shot cross-lingual EAE. 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. To address the above limitations, we propose the Transkimmer architecture, which learns to identify hidden state tokens that are not required by each layer. The softmax layer produces the distribution based on the dot products of a single hidden state and the embeddings of words in the vocabulary. Experiment results on standard datasets and metrics show that our proposed Auto-Debias approach can significantly reduce biases, including gender and racial bias, in pretrained language models such as BERT, RoBERTa and ALBERT. In dataset-transfer experiments on three social media datasets, we find that grounding the model in PHQ9's symptoms substantially improves its ability to generalize to out-of-distribution data compared to a standard BERT-based approach. Our proposed metric, RoMe, is trained on language features such as semantic similarity combined with tree edit distance and grammatical acceptability, using a self-supervised neural network to assess the overall quality of the generated sentence. Generating Data to Mitigate Spurious Correlations in Natural Language Inference Datasets.

Neural discrete reasoning (NDR) has shown remarkable progress in combining deep models with discrete reasoning. We present a benchmark suite of four datasets for evaluating the fairness of pre-trained language models and the techniques used to fine-tune them for downstream tasks. We focus on informative conversations, including business emails, panel discussions, and work channels. We demonstrate the effectiveness of MELM on monolingual, cross-lingual and multilingual NER across various low-resource levels. Recent parameter-efficient language model tuning (PELT) methods manage to match the performance of fine-tuning with much fewer trainable parameters and perform especially well when training data is limited.

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