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Our results shed light on understanding the storage of knowledge within pretrained Transformers. In this paper, we identify this challenge, and make a step forward by collecting a new human-to-human mixed-type dialog corpus. However, it is widely recognized that there is still a gap between the quality of the texts generated by models and the texts written by human. We present a direct speech-to-speech translation (S2ST) model that translates speech from one language to speech in another language without relying on intermediate text generation. In this work, we investigate the impact of vision models on MMT. In an educated manner wsj crossword puzzle. To this end, we formulate the Distantly Supervised NER (DS-NER) problem via Multi-class Positive and Unlabeled (MPU) learning and propose a theoretically and practically novel CONFidence-based MPU (Conf-MPU) approach.

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In this work, we analyze the learning dynamics of MLMs and find that it adopts sampled embeddings as anchors to estimate and inject contextual semantics to representations, which limits the efficiency and effectiveness of MLMs. However, such methods have not been attempted for building and enriching multilingual KBs. In an educated manner wsj crossword. 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. Chatter crossword clue. Simultaneous machine translation (SiMT) outputs translation while reading source sentence and hence requires a policy to decide whether to wait for the next source word (READ) or generate a target word (WRITE), the actions of which form a read/write path. In this work, we try to improve the span representation by utilizing retrieval-based span-level graphs, connecting spans and entities in the training data based on n-gram features.

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Moreover, we perform extensive ablation studies to motivate the design choices and prove the importance of each module of our method. 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. In an educated manner. We release our pretrained models, LinkBERT and BioLinkBERT, as well as code and data. In this paper, we propose a novel question generation method that first learns the question type distribution of an input story paragraph, and then summarizes salient events which can be used to generate high-cognitive-demand questions. FrugalScore: Learning Cheaper, Lighter and Faster Evaluation Metrics for Automatic Text Generation.

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However, in many scenarios, limited by experience and knowledge, users may know what they need, but still struggle to figure out clear and specific goals by determining all the necessary slots. Experiments on both AMR parsing and AMR-to-text generation show the superiority of our our knowledge, we are the first to consider pre-training on semantic graphs. We also perform extensive ablation studies to support in-depth analyses of each component in our framework. The proposed method has the following merits: (1) it addresses the fundamental problem that edges in a dependency tree should be constructed between subtrees; (2) the MRC framework allows the method to retrieve missing spans in the span proposal stage, which leads to higher recall for eligible spans. The most crucial facet is arguably the novelty — 35 U. We examine the effects of contrastive visual semantic pretraining by comparing the geometry and semantic properties of contextualized English language representations formed by GPT-2 and CLIP, a zero-shot multimodal image classifier which adapts the GPT-2 architecture to encode image captions. In an educated manner crossword clue. In a projective dependency tree, the largest subtree rooted at each word covers a contiguous sequence (i. e., a span) in the surface order.

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We testify our framework on WMT 2019 Metrics and WMT 2020 Quality Estimation benchmarks. After finetuning this model on the task of KGQA over incomplete KGs, our approach outperforms baselines on multiple large-scale datasets without extensive hyperparameter tuning. Towards Abstractive Grounded Summarization of Podcast Transcripts. To investigate this question, we apply mT5 on a language with a wide variety of dialects–Arabic. MarkupLM: Pre-training of Text and Markup Language for Visually Rich Document Understanding. This could be slow when the program contains expensive function calls. Existing works either limit their scope to specific scenarios or overlook event-level correlations. In an educated manner wsj crossword giant. In this position paper, we discuss the unique technological, cultural, practical, and ethical challenges that researchers and indigenous speech community members face when working together to develop language technology to support endangered language documentation and revitalization. Specifically, we build the entity-entity graph and span-entity graph globally based on n-gram similarity to integrate the information of similar neighbor entities into the span representation. "I myself was going to do what Ayman has done, " he said.

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Furthermore, we devise a cross-modal graph convolutional network to make sense of the incongruity relations between modalities for multi-modal sarcasm detection. Experiment results show that UDGN achieves very strong unsupervised dependency parsing performance without gold POS tags and any other external information. AdapLeR: Speeding up Inference by Adaptive Length Reduction. We show experimentally and through detailed result analysis that our stance detection system benefits from financial information, and achieves state-of-the-art results on the wt–wt dataset: this demonstrates that the combination of multiple input signals is effective for cross-target stance detection, and opens interesting research directions for future work. Lexical substitution is the task of generating meaningful substitutes for a word in a given textual context.

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We show how interactional data from 63 languages (26 families) harbours insights about turn-taking, timing, sequential structure and social action, with implications for language technology, natural language understanding, and the design of conversational interfaces. We find that active learning yields consistent gains across all SemEval 2021 Task 10 tasks and domains, but though the shared task saw successful self-trained and data augmented models, our systematic comparison finds these strategies to be unreliable for source-free domain adaptation. In this paper, we present the VHED (VIST Human Evaluation Data) dataset, which first re-purposes human evaluation results for automatic evaluation; hence we develop Vrank (VIST Ranker), a novel reference-free VIST metric for story evaluation. The evaluation shows that, even with much less data, DISCO can still outperform the state-of-the-art models in vulnerability and code clone detection tasks. Hahn shows that for languages where acceptance depends on a single input symbol, a transformer's classification decisions get closer and closer to random guessing (that is, a cross-entropy of 1) as input strings get longer and longer. Dominant approaches to disentangle a sensitive attribute from textual representations rely on learning simultaneously a penalization term that involves either an adversary loss (e. g., a discriminator) or an information measure (e. g., mutual information). He grew up in a very traditional home, but the area he lived in was a cosmopolitan, secular environment. With the development of biomedical language understanding benchmarks, AI applications are widely used in the medical field. In this position paper, we focus on the problem of safety for end-to-end conversational AI. Improving Event Representation via Simultaneous Weakly Supervised Contrastive Learning and Clustering. The collection begins with the works of Frederick Douglass and is targeted to include the works of W. E. B. Other dialects have been largely overlooked in the NLP community. With the rapid development of deep learning, Seq2Seq paradigm has become prevalent for end-to-end data-to-text generation, and the BLEU scores have been increasing in recent years.

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We show that an off-the-shelf encoder-decoder Transformer model can serve as a scalable and versatile KGE model obtaining state-of-the-art results for KG link prediction and incomplete KG question answering. Puts a limit on crossword clue. Additionally, our model improves the generation of long-form summaries from long government reports and Wikipedia articles, as measured by ROUGE scores. This paper proposes an effective dynamic inference approach, called E-LANG, which distributes the inference between large accurate Super-models and light-weight Swift models. Conversational agents have come increasingly closer to human competence in open-domain dialogue settings; however, such models can reflect insensitive, hurtful, or entirely incoherent viewpoints that erode a user's trust in the moral integrity of the system. ParaDetox: Detoxification with Parallel Data.

We compare the methods with respect to their ability to reduce the partial input bias while maintaining the overall performance. On the other hand, logic-based approaches provide interpretable rules to infer the target answer, but mostly work on structured data where entities and relations are well-defined. In this paper, we collect a dataset of realistic aspect-oriented summaries, AspectNews, which covers different subtopics about articles in news sub-domains. We then formulate the next-token probability by mixing the previous dependency modeling probability distributions with self-attention. As such an intermediate task, we perform clustering and train the pre-trained model on predicting the cluster test this hypothesis on various data sets, and show that this additional classification phase can significantly improve performance, mainly for topical classification tasks, when the number of labeled instances available for fine-tuning is only a couple of dozen to a few hundred. Mitchell of NBC News crossword clue. How to learn a better speech representation for end-to-end speech-to-text translation (ST) with limited labeled data? Here we present a simple demonstration-based learning method for NER, which lets the input be prefaced by task demonstrations for in-context learning. Displays despondency crossword clue. Consistent results are obtained as evaluated on a collection of annotated corpora.

We focus on scripts as they contain rich verbal and nonverbal messages, and two relevant messages originally conveyed by different modalities during a short time period may serve as arguments of a piece of commonsense knowledge as they function together in daily communications. BiTIIMT: A Bilingual Text-infilling Method for Interactive Machine Translation.

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