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This effectively alleviates overfitting issues originating from training domains. Our experiments in several traditional test domains (OntoNotes, CoNLL'03, WNUT '17, GUM) and a new large scale Few-Shot NER dataset (Few-NERD) demonstrate that on average, CONTaiNER outperforms previous methods by 3%-13% absolute F1 points while showing consistent performance trends, even in challenging scenarios where previous approaches could not achieve appreciable performance. Data-to-text generation focuses on generating fluent natural language responses from structured meaning representations (MRs).

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It could help the bots manifest empathy and render the interaction more engaging by demonstrating attention to the speaker's emotions. Compound once thought to cause food poisoning crossword clue. This makes them more accurate at predicting what a user will write. Our code and models are publicly available at An Interpretable Neuro-Symbolic Reasoning Framework for Task-Oriented Dialogue Generation. Recent unsupervised sentence compression approaches use custom objectives to guide discrete search; however, guided search is expensive at inference time. To mitigate the two issues, we propose a knowledge-aware fuzzy semantic parsing framework (KaFSP). To explain this discrepancy, through a toy theoretical example and empirical analysis on two crowdsourced CAD datasets, we show that: (a) while features perturbed in CAD are indeed robust features, it may prevent the model from learning unperturbed robust features; and (b) CAD may exacerbate existing spurious correlations in the data. Moreover, we are able to offer concrete evidence that—for some tasks—fastText can offer a better inductive bias than BERT. We show that the models are able to identify several of the changes under consideration and to uncover meaningful contexts in which they appeared. Experiments have been conducted on three datasets and results show that the proposed approach significantly outperforms both current state-of-the-art neural topic models and some topic modeling approaches enhanced with PWEs or PLMs. Slangvolution: A Causal Analysis of Semantic Change and Frequency Dynamics in Slang. In an educated manner crossword clue. Actions by the AI system may be required to bring these objects in view.

AdaLoGN: Adaptive Logic Graph Network for Reasoning-Based Machine Reading Comprehension. Informal social interaction is the primordial home of human language. QuoteR: A Benchmark of Quote Recommendation for Writing. Modeling Syntactic-Semantic Dependency Correlations in Semantic Role Labeling Using Mixture Models. Our main goal is to understand how humans organize information to craft complex answers. We validate our method on language modeling and multilingual machine translation. We adopt a stage-wise training approach that combines a source code retriever and an auto-regressive language model for programming language. In an educated manner wsj crossword puzzle answers. 0 on the Librispeech speech recognition task. Our evidence extraction strategy outperforms earlier baselines.

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It is also found that coherence boosting with state-of-the-art models for various zero-shot NLP tasks yields performance gains with no additional training. 10, Street 154, near the train station. We propose four different splitting methods, and evaluate our approach with BLEU and contrastive test sets. A central quest of probing is to uncover how pre-trained models encode a linguistic property within their representations. In an educated manner wsj crossword key. King's has access to: EIMA1: Music, Radio and The Stage. Across 5 Chinese NLU tasks, RoCBert outperforms strong baselines under three blackbox adversarial algorithms without sacrificing the performance on clean testset. Hence, we propose a task-free enhancement module termed as Heterogeneous Linguistics Graph (HLG) to enhance Chinese pre-trained language models by integrating linguistics knowledge. Altogether, our data will serve as a challenging benchmark for natural language understanding and support future progress in professional fact checking. To alleviate the token-label misalignment issue, we explicitly inject NER labels into sentence context, and thus the fine-tuned MELM is able to predict masked entity tokens by explicitly conditioning on their labels. We hope that our work can encourage researchers to consider non-neural models in future.

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. Probing for Labeled Dependency Trees. A Good Prompt Is Worth Millions of Parameters: Low-resource Prompt-based Learning for Vision-Language Models. Group of well educated men crossword clue. In this work, we propose a Multi-modal Multi-scene Multi-label Emotional Dialogue dataset, M 3 ED, which contains 990 dyadic emotional dialogues from 56 different TV series, a total of 9, 082 turns and 24, 449 utterances. We conduct extensive experiments to show the superior performance of PGNN-EK on the code summarization and code clone detection tasks.

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More surprisingly, ProtoVerb consistently boosts prompt-based tuning even on untuned PLMs, indicating an elegant non-tuning way to utilize PLMs. Spatial commonsense, the knowledge about spatial position and relationship between objects (like the relative size of a lion and a girl, and the position of a boy relative to a bicycle when cycling), is an important part of commonsense knowledge. Moreover, we demonstrate that only Vrank shows human-like behavior in its strong ability to find better stories when the quality gap between two stories is high. How to learn a better speech representation for end-to-end speech-to-text translation (ST) with limited labeled data?

The experimental results show that MultiHiertt presents a strong challenge for existing baselines whose results lag far behind the performance of human experts. This paper aims to distill these large models into smaller ones for faster inference and with minimal performance loss. We show that systems initially trained on few examples can dramatically improve given feedback from users on model-predicted answers, and that one can use existing datasets to deploy systems in new domains without any annotation effort, but instead improving the system on-the-fly via user feedback. Our lazy transition is deployed on top of UT to build LT (lazy transformer), where all tokens are processed unequally towards depth. One of its aims is to preserve the semantic content while adapting to the target domain. Lists KMD second among "top funk rap artists"—weird; I own a KMD album and did not know they were " FUNK-RAP. "

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Leveraging Wikipedia article evolution for promotional tone detection. We analyze how out-of-domain pre-training before in-domain fine-tuning achieves better generalization than either solution independently. Our results encourage practitioners to focus more on dataset quality and context-specific harms. The IMPRESSIONS section of a radiology report about an imaging study is a summary of the radiologist's reasoning and conclusions, and it also aids the referring physician in confirming or excluding certain diagnoses. CLUES: A Benchmark for Learning Classifiers using Natural Language Explanations. First experiments with the automatic classification of human values are promising, with F 1 -scores up to 0.

Comprehensive experiments on standard BLI datasets for diverse languages and different experimental setups demonstrate substantial gains achieved by our framework. Finally, we motivate future research in evaluation and classroom integration in the field of speech synthesis for language revitalization. This paper demonstrates that multilingual pretraining and multilingual fine-tuning are both critical for facilitating cross-lingual transfer in zero-shot translation, where the neural machine translation (NMT) model is tested on source languages unseen during supervised training. This work defines a new learning paradigm ConTinTin (Continual Learning from Task Instructions), in which a system should learn a sequence of new tasks one by one, each task is explained by a piece of textual instruction. We use HRQ-VAE to encode the syntactic form of an input sentence as a path through the hierarchy, allowing us to more easily predict syntactic sketches at test time. Pretrained multilingual models are able to perform cross-lingual transfer in a zero-shot setting, even for languages unseen during pretraining. Across 8 datasets representing 7 distinct NLP tasks, we show that when a template has high mutual information, it also has high accuracy on the task. Specifically, we propose a retrieval-augmented code completion framework, leveraging both lexical copying and referring to code with similar semantics by retrieval.

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Surprisingly, we find even Language models trained on text shuffled after subword segmentation retain some semblance of information about word order because of the statistical dependencies between sentence length and unigram probabilities. We found that existing fact-checking models trained on non-dialogue data like FEVER fail to perform well on our task, and thus, we propose a simple yet data-efficient solution to effectively improve fact-checking performance in dialogue. For all token-level samples, PD-R minimizes the prediction difference between the original pass and the input-perturbed pass, making the model less sensitive to small input changes, thus more robust to both perturbations and under-fitted training data. Towards building AI agents with similar abilities in language communication, we propose a novel rational reasoning framework, Pragmatic Rational Speaker (PRS), where the speaker attempts to learn the speaker-listener disparity and adjust the speech accordingly, by adding a light-weighted disparity adjustment layer into working memory on top of speaker's long-term memory system. EPT-X: An Expression-Pointer Transformer model that generates eXplanations for numbers. We first suggest three principles that may help NLP practitioners to foster mutual understanding and collaboration with language communities, and we discuss three ways in which NLP can potentially assist in language education. The Digital library comprises more than 3, 500 ebooks and textbooks on French Law, including all Codes Dalloz, Dalloz action, Glossaries, Précis, and a wide range of university textbooks and revision works that support both teaching and research. Few-shot Controllable Style Transfer for Low-Resource Multilingual Settings.

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In classic instruction following, language like "I'd like the JetBlue flight" maps to actions (e. g., selecting that flight). In this paper, we examine the summaries generated by two current models in order to understand the deficiencies of existing evaluation approaches in the context of the challenges that arise in the MDS task. Pre-trained language models have been recently shown to benefit task-oriented dialogue (TOD) systems. In my experience, only the NYTXW.

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