Predicting missing facts in a knowledge graph (KG) is crucial as modern KGs are far from complete. Cross-Cultural Comparison of the Account. Humble acknowledgment. Linguistic term for a misleading cognate crossword. Probing is popular to analyze whether linguistic information can be captured by a well-trained deep neural model, but it is hard to answer how the change of the encoded linguistic information will affect task performance. This provides a simple and robust method to boost SDP performance. Bootstrapping a contextual LM with only a subset of the metadata during training retains 85% of the achievable gain. Also, TV scripts contain content that does not directly pertain to the central plot but rather serves to develop characters or provide comic relief.
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Auto-Debias: Debiasing Masked Language Models with Automated Biased Prompts. To get the best of both worlds, in this work, we propose continual sequence generation with adaptive compositional modules to adaptively add modules in transformer architectures and compose both old and new modules for new tasks. Linguistic term for a misleading cognate crossword december. This paper provides valuable insights for the design of unbiased datasets, better probing frameworks and more reliable evaluations of pretrained language models. Therefore, in this paper, we design an efficient Transformer architecture, named Fourier Sparse Attention for Transformer (FSAT), for fast long-range sequence modeling. Dialogue agents can leverage external textual knowledge to generate responses of a higher quality.
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We demonstrate the utility of the corpus through its community use and its use to build language technologies that can provide the types of support that community members have expressed are desirable. But The Book of Mormon does contain what might be a very significant passage in relation to this event. In light of this it is interesting to consider an account from an old Irish history, Chronicum Scotorum. Using Cognates to Develop Comprehension in English. However, existing methods such as BERT model a single document, and do not capture dependencies or knowledge that span across documents. To provide adequate supervision, we propose simple yet effective heuristics for oracle extraction as well as a consistency loss term, which encourages the extractor to approximate the averaged dynamic weights predicted by the generator. In this paper, we propose a new method for dependency parsing to address this issue. Thirdly, we design a discriminator to evaluate the extraction result, and train both extractor and discriminator with generative adversarial training (GAT). Improving Chinese Grammatical Error Detection via Data augmentation by Conditional Error Generation. We crafted questions that some humans would answer falsely due to a false belief or misconception.
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Prix-LM integrates useful multilingual and KB-based factual knowledge into a single model. In this approach, we first construct the math syntax graph to model the structural semantic information, by combining the parsing trees of the text and formulas, and then design the syntax-aware memory networks to deeply fuse the features from the graph and text. Language Correspondences | Language and Communication: Essential Concepts for User Interface and Documentation Design | Oxford Academic. To handle the incomplete annotations, Conf-MPU consists of two steps. Furthermore, their performance does not translate well across tasks.
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Here we propose QCPG, a quality-guided controlled paraphrase generation model, that allows directly controlling the quality dimensions. Specifically, we first take the Stack-BERT layers as a primary encoder to grasp the overall semantic of the sentence and then fine-tune it by incorporating a lightweight Dynamic Re-weighting Adapter (DRA). However, our experiments reveal that improved verification performance does not necessarily translate to overall QA-based metric quality: In some scenarios, using a worse verification method — or using none at all — has comparable performance to using the best verification method, a result that we attribute to properties of the datasets. Natural language processing for sign language video—including tasks like recognition, translation, and search—is crucial for making artificial intelligence technologies accessible to deaf individuals, and is gaining research interest in recent years. However, we find that different faithfulness metrics show conflicting preferences when comparing different interpretations. What is false cognates in english. Empirical evaluation and analysis indicate that our framework obtains comparable performance under deployment-friendly model capacity. Surangika Ranathunga. Most low resource language technology development is premised on the need to collect data for training statistical models. Experiment results show that our model produces better question-summary hierarchies than comparisons on both hierarchy quality and content coverage, a finding also echoed by human judges. Unlike typical entity extraction datasets, FiNER-139 uses a much larger label set of 139 entity types. To identify multi-hop reasoning paths, we construct a relational graph from the sentence (text-to-graph generation) and apply multi-layer graph convolutions to it. Neural Label Search for Zero-Shot Multi-Lingual Extractive Summarization. We show that OCR monolingual data is a valuable resource that can increase performance of Machine Translation models, when used in backtranslation.
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Additionally, we leverage textual neighbors, generated by small perturbations to the original text, to demonstrate that not all perturbations lead to close neighbors in the embedding space. CASPI includes a mechanism to learn fine-grained reward that captures intention behind human response and also offers guarantee on dialogue policy's performance against a baseline. To address this challenge, we propose scientific claim generation, the task of generating one or more atomic and verifiable claims from scientific sentences, and demonstrate its usefulness in zero-shot fact checking for biomedical claims. However, text lacking context or missing sarcasm target makes target identification very difficult. Under the Morphosyntactic Lens: A Multifaceted Evaluation of Gender Bias in Speech Translation. We further observethat for text summarization, these metrics havehigh error rates when ranking current state-ofthe-art abstractive summarization systems. Metadata Shaping: A Simple Approach for Knowledge-Enhanced Language Models. Our approach first reduces the dimension of token representations by encoding them using a novel autoencoder architecture that uses the document's textual content in both the encoding and decoding phases. These social events may even alter the rate at which a given language undergoes change. Many recent works use BERT-based language models to directly correct each character of the input sentence.
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However, there does not exist a mechanism to directly control the model's focus. The proposed detector improves the current state-of-the-art performance in recognizing adversarial inputs and exhibits strong generalization capabilities across different NLP models, datasets, and word-level attacks. Data-to-text generation focuses on generating fluent natural language responses from structured meaning representations (MRs). It is important to note here, however, that the debate between the two sides doesn't seem to be so much on whether the idea of a common origin to all the world's languages is feasible or not. The overall complexity about the sequence length is reduced from 𝒪(L2) to 𝒪(Llog L). This architecture allows for unsupervised training of each language independently.
In spite of this success, kNN retrieval is at the expense of high latency, in particular for large datastores. ODE Transformer: An Ordinary Differential Equation-Inspired Model for Sequence Generation. Muthu Kumar Chandrasekaran. To explore this question, we present AmericasNLI, an extension of XNLI (Conneau et al., 2018) to 10 Indigenous languages of the Americas. Our results differ from previous, semantics-based studies and therefore help to contribute a more comprehensive – and, given the results, much more optimistic – picture of the PLMs' negation understanding. Leveraging the large training batch size of contrastive learning, we approximate the neighborhood of an instance via its K-nearest in-batch neighbors in the representation space. 0 and VQA-CP v2 datasets. Thus generalizations about language change are indeed generalizations based on the observation of limited data, none of which extends back to the time period in question. New York: McClure, Phillips & Co. - Wright, Peter. Specifically, we study several classes of reframing techniques for manual reformulation of prompts into more effective ones. The key novelty is that we directly involve the affected communities in collecting and annotating the data – as opposed to giving companies and governments control over defining and combatting hate speech. We introduce the task of fact-checking in dialogue, which is a relatively unexplored area. Via weakly supervised pre-training as well as the end-to-end fine-tuning, SR achieves new state-of-the-art performance when combined with NSM (He et al., 2021), a subgraph-oriented reasoner, for embedding-based KBQA methods.
Particularly, the proposed approach allows the auto-regressive decoder to refine the previously generated target words and generate the next target word synchronously. Single Model Ensemble for Subword Regularized Models in Low-Resource Machine Translation. 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. Current methods typically achieve cross-lingual retrieval by learning language-agnostic text representations in word or sentence level. In this work, we test the hypothesis that the extent to which a model is affected by an unseen textual perturbation (robustness) can be explained by the learnability of the perturbation (defined as how well the model learns to identify the perturbation with a small amount of evidence). Towards building intelligent dialogue agents, there has been a growing interest in introducing explicit personas in generation models. Extracting Latent Steering Vectors from Pretrained Language Models. In this work, we present DPT, the first prompt tuning framework for discriminative PLMs, which reformulates NLP tasks into a discriminative language modeling problem. However, such models risk introducing errors into automatically simplified texts, for instance by inserting statements unsupported by the corresponding original text, or by omitting key information. RoMe: A Robust Metric for Evaluating Natural Language Generation. Existing research works in MRC rely heavily on large-size models and corpus to improve the performance evaluated by metrics such as Exact Match (EM) and F1.
Originating from the interpretation that data augmentation essentially constructs the neighborhoods of each training instance, we, in turn, utilize the neighborhood to generate effective data augmentations. Investigating Non-local Features for Neural Constituency Parsing. Specifically, we share the weights of bottom layers across all models and apply different perturbations to the hidden representations for different models, which can effectively promote the model diversity. This work reveals the ability of PSHRG in formalizing a syntax–semantics interface, modelling compositional graph-to-tree translations, and channelling explainability to surface realization. Empirical results demonstrate the effectiveness of our method in both prompt responding and translation quality. Comparatively little work has been done to improve the generalization of these models through better optimization. Text summarization models are approaching human levels of fidelity. The avoidance of taboo expressions may result in frequent change, indeed "a constant turnover in vocabulary" (, 294-95).
We annotate data across two domains of articles, earthquakes and fraud investigations, where each article is annotated with two distinct summaries focusing on different aspects for each domain. It contains 58K video and question pairs that are generated from 10K videos from 20 different virtual environments, containing various objects in motion that interact with each other and the scene. In this work, we introduce a gold-standard set of dependency parses for CFQ, and use this to analyze the behaviour of a state-of-the art dependency parser (Qi et al., 2020) on the CFQ dataset. Producing this list involves subjective decisions and it might be difficult to obtain for some types of biases. In modern recommender systems, there are usually comments or reviews from users that justify their ratings for different items. User language data can contain highly sensitive personal content. Images are sourced from both static pictures and video benchmark several state-of-the-art models, including both cross-encoders such as ViLBERT and bi-encoders such as CLIP, on results reveal that these models dramatically lag behind human performance: the best variant achieves an accuracy of 20. These approaches, however, exploit general dialogic corpora (e. g., Reddit) and thus presumably fail to reliably embed domain-specific knowledge useful for concrete downstream TOD domains. Extensive experiments demonstrate SR achieves significantly better retrieval and QA performance than existing retrieval methods.
Our source code is available at Cross-Utterance Conditioned VAE for Non-Autoregressive Text-to-Speech. We analyze different choices to collect knowledge-aligned dialogues, represent implicit knowledge, and transition between knowledge and dialogues. Reinforced Cross-modal Alignment for Radiology Report Generation.
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Copy staff styles between projects. Trim or silence audio files. But we're going to let you in on a little secret: We drummers love the jokes.
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Still another week later the poor guy is back: "Doc, STILL nothing!