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July 4, 2024, 9:19 pm

Existing methods encode text and label hierarchy separately and mix their representations for classification, where the hierarchy remains unchanged for all input text. ConTinTin: Continual Learning from Task Instructions. Then we study the contribution of modified property through the change of cross-language transfer results on target language. Cross-domain Named Entity Recognition via Graph Matching. Unlike previously proposed datasets, WikiEvolve contains seven versions of the same article from Wikipedia, from different points in its revision history; one with promotional tone, and six without it. We conduct multilingual zero-shot summarization experiments on MLSUM and WikiLingua datasets, and we achieve state-of-the-art results using both human and automatic evaluations across these two datasets. Our findings establish a firmer theoretical foundation for bottom-up probing and highlight richer deviations from human priors. The traditional view of the Babel account, as has been mentioned, is that the confusion of languages caused the people to disperse. Then he orders trees to be cut down and piled one upon another. In particular, we first explore semantic dependencies between clauses and keywords extracted from the document that convey fine-grained semantic features, obtaining keywords enhanced clause representations. Linguistic term for a misleading cognate crossword october. Experiments show that our method achieves 2. Progress with supervised Open Information Extraction (OpenIE) has been primarily limited to English due to the scarcity of training data in other languages.

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Modeling Syntactic-Semantic Dependency Correlations in Semantic Role Labeling Using Mixture Models. Modeling Multi-hop Question Answering as Single Sequence Prediction. Our model obtains a boost of up to 2. For Non-autoregressive NMT, we demonstrate it can also produce consistent performance gains, i. e., up to +5. Newsday Crossword February 20 2022 Answers –. Existing approaches waiting-and-translating for a fixed duration often break the acoustic units in speech, since the boundaries between acoustic units in speech are not even. On the downstream tabular inference task, using only the automatically extracted evidence as the premise, our approach outperforms prior benchmarks. A typical example is when using CNN/Daily Mail dataset for controllable text summarization, there is no guided information on the emphasis of summary sentences. Accordingly, we first study methods reducing the complexity of data distributions. We present Knowledge Distillation with Meta Learning (MetaDistil), a simple yet effective alternative to traditional knowledge distillation (KD) methods where the teacher model is fixed during training.

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Few-Shot Relation Extraction aims at predicting the relation for a pair of entities in a sentence by training with a few labelled examples in each relation. In this study, we analyze the training dynamics of the token embeddings focusing on rare token embedding. Our experiments show that the state-of-the-art models are far from solving our new task. Our work indicates the necessity of decomposing question type distribution learning and event-centric summary generation for educational question generation. Linguistic term for a misleading cognate crossword puzzle. PPT: Pre-trained Prompt Tuning for Few-shot Learning. Label Semantic Aware Pre-training for Few-shot Text Classification. Mining event-centric opinions can benefit decision making, people communication, and social good. This phenomenon is similar to the sparsity of the human brain, which drives research on functional partitions of the human brain. Extensive experimental results indicate that compared with previous code search baselines, CoSHC can save more than 90% of retrieval time meanwhile preserving at least 99% of retrieval accuracy.

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Our best single sequence tagging model that is pretrained on the generated Troy- datasets in combination with the publicly available synthetic PIE dataset achieves a near-SOTA result with an F0. But what kind of representational spaces do these models construct? Experiments on En-Vi and De-En tasks show that our method can outperform strong baselines under all latency. In this work, we revisit LM-based constituency parsing from a phrase-centered perspective. Predicting the subsequent event for an existing event context is an important but challenging task, as it requires understanding the underlying relationship between events. In the inference phase, the trained extractor selects final results specific to the given entity category. Most importantly, we show that current neural language models can automatically generate new RoTs that reasonably describe previously unseen interactions, but they still struggle with certain scenarios. This work connects language model adaptation with concepts of machine learning theory. 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. Across 13 languages, our proposed method identifies the best source treebank 94% of the time, outperforming competitive baselines and prior work. To incorporate a rare word definition as a part of input, we fetch its definition from the dictionary and append it to the end of the input text sequence. Language Correspondences | Language and Communication: Essential Concepts for User Interface and Documentation Design | Oxford Academic. Notably, our approach sets the single-model state-of-the-art on Natural Questions.

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In this work, we focus on enhancing language model pre-training by leveraging definitions of the rare words in dictionaries (e. g., Wiktionary). Comprehensive experiments on two code generation tasks demonstrate the effectiveness of our proposed approach, improving the success rate of compilation from 44. Linguistic term for a misleading cognate crossword daily. In this paper, we propose to use it for data augmentation in NLP. The model takes as input multimodal information including the semantic, phonetic and visual features. Evaluating Factuality in Text Simplification.

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Results on six English benchmarks and one Chinese dataset show that our model can achieve competitive performance and interpretability. The experimental results across all the domain pairs show that explanations are useful for calibrating these models, boosting accuracy when predictions do not have to be returned on every example. Mohammad Javad Hosseini. To address this issue, we propose a novel framework that unifies the document classifier with handcrafted features, particularly time-dependent novelty scores. Moreover, analysis shows that XLM-E tends to obtain better cross-lingual transferability. NewsDay Crossword February 20 2022 Answers. This work describes IteraTeR: the first large-scale, multi-domain, edit-intention annotated corpus of iteratively revised text.

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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. We report promising qualitative results for several attribute transfer tasks (sentiment transfer, simplification, gender neutralization, text anonymization) all without retraining the model. MELM: Data Augmentation with Masked Entity Language Modeling for Low-Resource NER. Muhammad Abdul-Mageed. There are a few dimensions in the monolingual BERT with high contributions to the anisotropic distribution. Our approach interpolates instances from different language pairs into joint 'crossover examples' in order to encourage sharing input and output spaces across languages. Without altering the training strategy, the task objective can be optimized on the selected subset. Most existing news recommender systems conduct personalized news recall and ranking separately with different models. In this paper, we introduce multimodality to STI and present Multimodal Sarcasm Target Identification (MSTI) task. We release an evaluation scheme and dataset for measuring the ability of NMT models to translate gender morphology correctly in unambiguous contexts across syntactically diverse sentences. We propose to use about one hour of annotated data to design an automatic speech recognition system for each language. We propose a new method for projective dependency parsing based on headed spans.

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Although the Chinese language has a long history, previous Chinese natural language processing research has primarily focused on tasks within a specific era. We curate CICERO, a dataset of dyadic conversations with five types of utterance-level reasoning-based inferences: cause, subsequent event, prerequisite, motivation, and emotional reaction. Although many previous studies try to incorporate global information into NMT models, there still exist limitations on how to effectively exploit bidirectional global context. Hybrid Semantics for Goal-Directed Natural Language Generation. To this end, we propose ELLE, aiming at efficient lifelong pre-training for emerging data.
Our code is available at. FCLC first train a coarse backbone model as a feature extractor and noise estimator. Open-Domain Conversation with Long-Term Persona Memory. We conduct extensive experiments on both rich-resource and low-resource settings involving various language pairs, including WMT14 English→{German, French}, NIST Chinese→English and multiple low-resource IWSLT translation tasks. Especially, MGSAG outperforms other models significantly in the condition of position-insensitive data. SRL4E – Semantic Role Labeling for Emotions: A Unified Evaluation Framework. Machine translation output notably exhibits lower lexical diversity, and employs constructs that mirror those in the source sentence. Thorough analyses are conducted to gain insights into each component. Towards Collaborative Neural-Symbolic Graph Semantic Parsing via Uncertainty. Mark Hasegawa-Johnson. In recent years, an approach based on neural textual entailment models has been found to give strong results on a diverse range of tasks. Controlled text perturbation is useful for evaluating and improving model generalizability.

Pre-trained language models have been recently shown to benefit task-oriented dialogue (TOD) systems. To reach that goal, we first make the inherent structure of language and visuals explicit by a dependency parse of the sentences that describe the image and by the dependencies between the object regions in the image, respectively. To address this issue, we introduce an evaluation framework that improves previous evaluation procedures in three key aspects, i. e., test performance, dev-test correlation, and stability. Cross-domain NER is a practical yet challenging problem since the data scarcity in the real-world scenario. Confounding the human language was merely an assurance that the Babel incident would not be repeated. Recently, exploiting dependency syntax information with graph neural networks has been the most popular trend. Why don't people use character-level machine translation? Eventually, however, such euphemistic substitutions acquire the negative connotations and need to be replaced themselves. 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. Latest studies on adversarial attacks achieve high attack success rates against PrLMs, claiming that PrLMs are not robust. To address this challenge, we propose KenMeSH, an end-to-end model that combines new text features and a dynamic knowledge-enhanced mask attention that integrates document features with MeSH label hierarchy and journal correlation features to index MeSH terms. Here, we test this assumption of political users and show that commonly-used political-inference models do not generalize, indicating heterogeneous types of political users. Existing methods for logical reasoning mainly focus on contextual semantics of text while struggling to explicitly model the logical inference process.