Unnamed: 0 int64 0 886 | question stringlengths 19 151 | answer stringlengths 1 1.08k | abstract stringlengths 279 2.02k | introduction stringlengths 52 9.04k ⌀ |
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845 | What dataset they use for evaluation? | The same 2K set from Gigaword used in BIBREF7 | Currency trading (Forex) is the largest world market in terms of volume. We analyze trading and tweeting about the EUR-USD currency pair over a period of three years. First, a large number of tweets were manually labeled, and a Twitter stance classification model is constructed. The model then classifies all the tweets... | Currency trading (Forex) is the largest world market in terms of volume. We analyze trading and tweeting about the EUR-USD currency pair over a period of three years. First, a large number of tweets were manually labeled, and a Twitter stance classification model is constructed. The model then classifies all the tweets... |
847 | Which regions of the United States do they consider? | all regions except those that are colored black | Information distribution by electronic messages is a privileged means of transmission for many businesses and individuals, often under the form of plain-text tables. As their number grows, it becomes necessary to use an algorithm to extract text and numbers instead of a human. Usual methods are focused on regular expre... | Today most of business-related information is transmitted in an electronic form, such as emails. Therefore, converting these messages into an easily analyzable representation could open numerous business opportunities, as a lot of them are not used fully because of the difficulty to build bespoke parsing methods. In pa... |
849 | How is performance measured? | they use ROC curves and cross-validation | Recent advances in modern Natural Language Processing (NLP) research have been dominated by the combination of Transfer Learning methods with large-scale language models, in particular based on the Transformer architecture. With them came a paradigm shift in NLP with the starting point for training a model on a downstr... | In the past 18 months, advances on many Natural Language Processing (NLP) tasks have been dominated by deep learning models and, more specifically, the use of Transfer Learning methods BIBREF0 in which a deep neural network language model is pretrained on a web-scale unlabelled text dataset with a general-purpose train... |
854 | What is novel in author's approach? | They use self-play learning , optimize the model for specific metrics, train separate models per user, use model and response classification predictors, and filter the dataset to obtain higher quality training data. | Fine-tuning language models, such as BERT, on domain specific corpora has proven to be valuable in domains like scientific papers and biomedical text. In this paper, we show that fine-tuning BERT on legal documents similarly provides valuable improvements on NLP tasks in the legal domain. Demonstrating this outcome is ... | Businesses rely on contracts to capture critical obligations with other parties, such as: scope of work, amounts owed, and cancellation policies. Various efforts have gone into automatically extracting and classifying these terms. These efforts have usually been modeled as: classification, entity and relation extractio... |
855 | How large is the Dialog State Tracking Dataset? | 1,618 training dialogs, 500 validation dialogs, and 1,117 test dialogs | This is the application document for the 2019 Amazon Alexa competition. We give an overall vision of our conversational experience, as well as a sample conversation that we would like our dialog system to achieve by the end of the competition. We believe personalization, knowledge, and self-play are important component... | Prompt: What is your team’s vision for your Socialbot? How do you want your customers to feel at the end of an interaction with your socialbot? How would your team measure success in competition? Our vision is made up of the following main points: 1. A natural, engaging, and knowledge-powered conversational experience.... |
856 | What dataset is used for train/test of this method? | Training datasets: TTS System dataset and embedding selection dataset. Evaluation datasets: Common Prosody Errors dataset and LFR dataset. | Traditional dialog systems used in goal-oriented applications require a lot of domain-specific handcrafting, which hinders scaling up to new domains. End-to-end dialog systems, in which all components are trained from the dialogs themselves, escape this limitation. But the encouraging success recently obtained in chit-... | null |
857 | How much is the gap between using the proposed objective and using only cross-entropy objective? | The mixed objective improves EM by 2.5% and F1 by 2.2% | Recent advances in Text-to-Speech (TTS) have improved quality and naturalness to near-human capabilities when considering isolated sentences. But something which is still lacking in order to achieve human-like communication is the dynamic variations and adaptability of human speech. This work attempts to solve the prob... | Corresponding author email: tshubhi@amazon.com. Paper submitted to IEEE ICASSP 2020 Recent advances in TTS have improved the achievable synthetic speech naturalness to near human-like capabilities BIBREF0, BIBREF1, BIBREF2, BIBREF3. This means that for simple sentences, or for situations in which we can correctly predi... |
859 | How many domains of ontologies do they gather data from? | 5 domains: software, stuff, african wildlife, healthcare, datatypes | A core step in statistical data-to-text generation concerns learning correspondences between structured data representations (e.g., facts in a database) and associated texts. In this paper we aim to bootstrap generators from large scale datasets where the data (e.g., DBPedia facts) and related texts (e.g., Wikipedia ab... | A core step in statistical data-to-text generation concerns learning correspondences between structured data representations (e.g., facts in a database) and paired texts BIBREF0 , BIBREF1 , BIBREF2 . These correspondences describe how data representations are expressed in natural language (content realisation) but also... |
861 | what is the practical application for this paper? | Improve existing NLP methods. Improve linguistic analysis. Measure impact of word normalization tools. | Answering science questions posed in natural language is an important AI challenge. Answering such questions often requires non-trivial inference and knowledge that goes beyond factoid retrieval. Yet, most systems for this task are based on relatively shallow Information Retrieval (IR) and statistical correlation techn... | Answering questions posed in natural language is a fundamental AI task, with a large number of impressive QA systems built over the years. Today's Internet search engines, for instance, can successfully retrieve factoid style answers to many natural language queries by efficiently searching the Web. Information Retriev... |
863 | What's the method used here? | Two neural networks: an extractor based on an encoder (BERT) and a decoder (LSTM Pointer Network BIBREF22) and an abstractor identical to the one proposed in BIBREF8. | Interpretability of a predictive model is a powerful feature that gains the trust of users in the correctness of the predictions. In word sense disambiguation (WSD), knowledge-based systems tend to be much more interpretable than knowledge-free counterparts as they rely on the wealth of manually-encoded elements repres... | The notion of word sense is central to computational lexical semantics. Word senses can be either encoded manually in lexical resources or induced automatically from text. The former knowledge-based sense representations, such as those found in the BabelNet lexical semantic network BIBREF0 , are easily interpretable by... |
864 | By how much does their method outperform state-of-the-art OOD detection? | AE-HCN outperforms by 17%, AE-HCN-CNN outperforms by 20% on average | As an attempt to combine extractive and abstractive summarization, Sentence Rewriting models adopt the strategy of extracting salient sentences from a document first and then paraphrasing the selected ones to generate a summary. However, the existing models in this framework mostly rely on sentence-level rewards or sub... | The task of automatic text summarization aims to compress a textual document to a shorter highlight while keeping salient information of the original text. In general, there are two ways to do text summarization: Extractive and Abstractive BIBREF0. Extractive approaches generate summaries by selecting salient sentences... |
865 | What are dilated convolutions? | Similar to standard convolutional networks but instead they skip some input values effectively operating on a broader scale. | Neural dialog models often lack robustness to anomalous user input and produce inappropriate responses which leads to frustrating user experience. Although there are a set of prior approaches to out-of-domain (OOD) utterance detection, they share a few restrictions: they rely on OOD data or multiple sub-domains, and th... | Recently, there has been a surge of excitement in developing chatbots for various purposes in research and enterprise. Data-driven approaches offered by common bot building platforms (e.g. Google Dialogflow, Amazon Alexa Skills Kit, Microsoft Bot Framework) make it possible for a wide range of users to easily create di... |
868 | what are the three methods presented in the paper? | Optimized TF-IDF, iterated TF-IDF, BERT re-ranking. | There is a lot of research interest in encoding variable length sentences into fixed length vectors, in a way that preserves the sentence meanings. Two common methods include representations based on averaging word vectors, and representations based on the hidden states of recurrent neural networks such as LSTMs. The s... | Parameters of the encoder-decoder were tuned on a dedicated validation set. We experienced with different learning rates (0.1, 0.01, 0.001), dropout-rates (0.1, 0.2, 0.3, 0.5) BIBREF11 and optimization techniques (AdaGrad BIBREF6 , AdaDelta BIBREF30 , Adam BIBREF15 and RMSprop BIBREF29 ). We also experimented with diff... |
869 | what datasets did the authors use? | Kaggle
Subversive Kaggle
Wikipedia
Subversive Wikipedia
Reddit
Subversive Reddit | The TextGraphs-13 Shared Task on Explanation Regeneration asked participants to develop methods to reconstruct gold explanations for elementary science questions. Red Dragon AI's entries used the language of the questions and explanation text directly, rather than a constructing a separate graph-like representation. Ou... | The Explanation Regeneration shared task asked participants to develop methods to reconstruct gold explanations for elementary science questions BIBREF1, using a new corpus of gold explanations BIBREF2 that provides supervision and instrumentation for this multi-hop inference task. Each explanation is represented as an... |
874 | How much performance improvements they achieve on SQuAD? | Compared to baselines SAN (Table 1) shows improvement of 1.096% on EM and 0.689% F1. Compared to other published SQuAD results (Table 2) SAN is ranked second. | In this paper, we describe our system which participates in the shared task of Hate Speech Detection on Social Networks of VLSP 2019 evaluation campaign. We are provided with the pre-labeled dataset and an unlabeled dataset for social media comments or posts. Our mission is to pre-process and build machine learning mod... | In recent years, social networking has grown and become prevalent with every people, it makes easy for people to interact and share with each other. However, every problem has two sides. It also has some negative issues, hate speech is a hot topic in the domain of social media. With the freedom of speech on social netw... |
879 | What is the baseline? | The baseline is a multi-task architecture inspired by another paper. | We analyze the language learned by an agent trained with reinforcement learning as a component of the ActiveQA system [Buck et al., 2017]. In ActiveQA, question answering is framed as a reinforcement learning task in which an agent sits between the user and a black box question-answering system. The agent learns to ref... | BIBREF0 propose a reinforcement learning framework for question answering, called active question answering (ActiveQA), that aims to improve answering by systematically perturbing input questions (cf. BIBREF1 ). Figure 1 depicts the generic agent-environment framework. The agent (AQA) interacts with the environment (E... |
881 | What does recurrent deep stacking network do? | Stacks and joins outputs of previous frames with inputs of the current frame | Interest in larger-context neural machine translation, including document-level and multi-modal translation, has been growing. Multiple works have proposed new network architectures or evaluation schemes, but potentially helpful context is still sometimes ignored by larger-context translation models. In this paper, we ... | Despite its rapid adoption by academia and industry and its recent success BIBREF0 , neural machine translation has been found largely incapable of exploiting additional context other than the current source sentence. This incapability stems from the fact that larger-context machine translation systems tend to ignore a... |
882 | What is the reward model for the reinforcement learning appraoch? | reward 1 for successfully completing the task, with a discount by the number of turns, and reward 0 when fail | This paper presented our work on applying Recurrent Deep Stacking Networks (RDSNs) to Robust Automatic Speech Recognition (ASR) tasks. In the paper, we also proposed a more efficient yet comparable substitute to RDSN, Bi- Pass Stacking Network (BPSN). The main idea of these two models is to add phoneme-level informatio... | Ever since the introduction of Deep Neural Networks (DNNs) to Automatic Speech Recognition (ASR) tasks BIBREF0 , researchers had been trying to use additional inputs to the raw input features. We extracted features that are more representative using the first and second order differentiates of the raw input features. A... |
883 | Does this paper propose a new task that others can try to improve performance on? | No, there has been previous work on recognizing social norm violation. | End-to-end learning of recurrent neural networks (RNNs) is an attractive solution for dialog systems; however, current techniques are data-intensive and require thousands of dialogs to learn simple behaviors. We introduce Hybrid Code Networks (HCNs), which combine an RNN with domain-specific knowledge encoded as softwa... | Task-oriented dialog systems help a user to accomplish some goal using natural language, such as making a restaurant reservation, getting technical support, or placing a phonecall. Historically, these dialog systems have been built as a pipeline, with modules for language understanding, state tracking, action selection... |
884 | What task do they evaluate on? | Fill-in-the-blank natural language questions | Social norms are shared rules that govern and facilitate social interaction. Violating such social norms via teasing and insults may serve to upend power imbalances or, on the contrary reinforce solidarity and rapport in conversation, rapport which is highly situated and context-dependent. In this work, we investigate ... | Social norms are informal understandings that govern human behavior. They serve as the basis for our beliefs and expectations about others, and are instantiated in human-human conversation through verbal and nonverbal behaviors BIBREF0 , BIBREF1 . There is considerable body of work on modeling socially normative behavi... |
886 | How many feature maps are generated for a given triple? | 3 feature maps for a given tuple | Natural language understanding has recently seen a surge of progress with the use of sentence encoders like ELMo (Peters et al., 2018a) and BERT (Devlin et al., 2019) which are pretrained on variants of language modeling. We conduct the first large-scale systematic study of candidate pretraining tasks, comparing 19 dif... | State-of-the-art models for natural language processing (NLP) tasks like translation, question answering, and parsing include components intended to extract representations for the meaning and contents of each input sentence. These sentence encoder components are typically trained directly for the target task at hand. ... |
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