Convkn at semeval

Temporal gates play a significant role in modern recurrent-based neural encoders, enabling fine-grained control over recursive compositional operations over time. In recurrent models such as the long short-term memory LSTMtemporal gates control the amount of information retained or discarded over time, not only playing an important role in influencing the learned representations but also serving as a protection against vanishing gradients.

This paper explores the idea of learning temporal gates for sequence pairs question and answerjointly influencing the learned representations in a pairwise manner.

In our approach, temporal gates are learned via 1D convolutional layers and then subsequently cross applied across question and answer for joint learning. Empirically, we show that this conceptually simple sharing of temporal gates can lead to competitive performance across multiple benchmarks.

Intuitively, what our network achieves can be interpreted as learning representations of question and answer pairs that are aware of what each other is remembering or forgetting, i.

Via extensive experiments, we show that our proposed model achieves state-of-the-art performance on two community-based QA datasets and competitive performance on one factoid-based QA dataset. Yi Tay. Luu Anh Tuan. Siu Cheung Hui. Plain recurrent networks greatly suffer from the vanishing gradient prob In this paper, the answer selection problem in community question answer The dominant neural architectures in question answer retrieval are based The field of lung nodule detection and cancer prediction has been rapidl Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.

Learning-to-rank for QA question answering is a long standing problem in NLP and IR research which benefits a wide assortment of subtasks such as community-based question answering CQA and factoid based question answering. The problem is mainly concerned with computing relevance scores between questions and prospective answers and subsequently ranking them.

Across the rich history of answer or document retrieval, statistical approaches based on feature engineering are commonly adopted. These models are largely based on complex lexical and syntactic features [ Wang and ManningZhou et al.

Today, we see a shift into neural question answering. Specifically, end-to-end deep neural networks are used for both automatically learning features and scoring of QA pairs.

Popular neural encoders for neural question answering include long short-term memory LSTM networks. The key idea behind neural encoders is to learn to compose. While it is possible to encode questions and answers independently, and later merge them with multi-layer perceptrons MLP.

Temporal gates form the cornerstone of modern recurrent neural encoders such as long short-term memory LSTM or gated recurrent units GRUserving as one of the key mitigation strategies against vanishing gradients.

In these models, temporal gates control the inner recursive loop along with the amount of information being discarded and retained at each time step, allowing fine-grained control over the semantic compositionality of learned representations.

Our work explores the idea of jointly learning temporal gates for sequence pairs, aiming to learn fine-grained representations of QA pairs which benefit from information pertaining to what each other is remembering or forgetting.We show that the task of question answering QA can significantly benefit from the transfer learning of models trained on a different large, fine-grained QA dataset.

For WikiQA, our model outperforms the previous best model by more than 8 learning lexical and syntactic information than coarser supervision, through quantitative results and visual analysis.

We also show that a similar transfer learning procedure achieves the state of the art on an entailment task. Sewon Min. Minjoon Seo. Hannaneh Hajishirzi.

convkn at semeval

Although transfer learning has been shown to be successful for tasks lik Recently, bidirectional recurrent neural network BRNN has been widely We study approaches to improve fine-grained short answer Question Answer Question classification QC is a prime constituent of automated questio We present a new approach for transferring knowledge from groups to indi An identity denotes the role an individual or a group plays in highly di Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.

Question answering QA is a long-standing challenge in NLP, and the community has introduced several paradigms and datasets for the task over the past few years.

These paradigms differ from each other in the type of questions and answers and the size of the training data, from a few hundreds to millions of examples. We are particularly interested in the context-aware QA paradigm, where the answer to each question can be obtained by referring to its accompanying context paragraph or a list of sentences.

Under this setting, the two most notable types of supervisions are coarse sentence-level and fine-grained span-level. We demonstrate that the target task not only benefits from the scale of the source dataset but also the capability of the fine-grained span supervision to better learn syntactic and lexical information. Modern machine learning models, especially deep neural networksoften significantly benefit from transfer learning.

In computer visiondeep convolutional neural networks trained on a large image classification dataset such as ImageNet. In natural language processingdomain adaptation has traditionally been an important topic for syntactic parsing. With the popularity of distributed representationpre-trained word embedding models such as word2vec. There have been several QA paradigms in NLP, which can be categorized by the context and supervision used to answer questions.

The answer types in these datasets are largely divided into three categories: sentence-level, in-context span, and generation. In this paper, we specifically focus on the former two and show that span-supervised models can better learn syntactic and lexical features. Among these datasets, we briefly describe three QA datasets to be used for the experiments in this paper.

Each example is a pair of context paragraph from Wikipedia and a question created by a human, and the answer is a span in the context. We split the context paragraph into sentences and formulate the task as classifying whether each sentence contains the answer. This enables us to make a fair comparison between pretraining with span-supervised and sentence-supervised QA datasets.

The task is to classify whether each sentence provides the answer to the query. Each example consists of a community question by a user and 10 comments. The task is to classify whether each comment is relevant to the question. Each example consists of a hypothesis and a premise, and the goal is to determine if the premise is entailed by, contradicts, or is neutral to the hypothesis hence classification problem.

The inputs to the model are a question qand a context paragraph x. Here, y s t a r t i and y e n d i are start and end position probabilities of i -th element, respectively. Here, we briefly describe the answer module which is important for transfer learning to sentence-level QA.Read this paper on arXiv.

SemEval-2021

Many state-of-the-art deep learning models for question answer retrieval are highly complex, often having a huge number of parameters or complicated word interaction mechanisms.

This paper studies if it is possible to achieve equally competitive performance with smaller and faster neural architectures. Overall, our proposed approach is a simple neural network that performs question-answer matching and ranking in Hyperbolic space. We show that QA embeddings learned in Hyperbolic space results in highly competitive performance on multiple benchmarks, outperforming models with significantly much larger parameters.

Neural ranking models are commonplace in many modern question answering QA systems Severyn and Moschitti, ; He and Lin, In these applications, the problem of question answering is concerned with learning to rank candidate answers in response to questions. For this purpose, a wide assortment of neural ranking architectures have been proposed. The key and most basic intuition pertaining to many of these models are as follows: Firstly, representations of questions and answers are first learned via a neural encoder such as the long short-term memory LSTM Hochreiter and Schmidhuber, network or convolutional neural network CNN.

Secondly, these representations of questions and answers are composed by an interaction function to produce an overall matching score. The design of the interaction function between question and answer representations lives at the heart of deep learning QA research. Apparently, it seems to be well-established that grid-based matching is essential to good performance. Notably, these new innovations come with trade-offs such as huge computational cost that lead to significantly longer training times and also a larger memory footprint.

Additionally, it is good to consider that the base neural encoder employed also contributes to the computational cost of these neural ranking models, e. In this paper, we propose an extremely simple neural ranking model for question answering that achieves highly competitive results on several benchmarks with only a fraction of the runtime and only 40KK parameters as opposed to millions.

Our neural ranking models the relationships between QA pairs in Hyperbolic space instead of Euclidean space.

Hyperbolic space is an embedding space with a constant negative curvature in which the distance towards the border is increasing exponentially. Intuitively, this makes it suitable for learning embeddings that reflect a natural hierarchy e. In our early empirical experiments, we discovered that a simple feed forward neural network trained in Hyperbolic space is capable of outperforming more sophisticated models on several standard benchmark datasets.

We believe that this can be attributed to two reasons. Firstly, latent hierarchies are prominent in QA. Aside from the natural hierarchy of questions and answers, conceptual hierarchies also exist. The key contributions in this paper are as follows:.

We propose a new neural ranking model for ranking of question answer pairs. For the first time, our proposed model, HyperQAperforms matching of question and answer in hyperbolic space.SemEval Sem antic Eval uation is an ongoing series of evaluations of computational semantic analysis systems; it evolved from the Senseval word sense evaluation series.

The evaluations are intended to explore the nature of meaning in language. While meaning is intuitive to humans, transferring those intuitions to computational analysis has proved elusive.

This series of evaluations is providing a mechanism to characterize in more precise terms exactly what is necessary to compute in meaning. As such, the evaluations provide an emergent mechanism to identify the problems and solutions for computations with meaning. These exercises have evolved to articulate more of the dimensions that are involved in our use of language.

They began with apparently simple attempts to identify word senses computationally. They have evolved to investigate the interrelationships among the elements in a sentence e. The purpose of the SemEval and Senseval exercises is to evaluate semantic analysis systems. The first three evaluations, Senseval-1 through Senseval-3, were focused on word sense disambiguation WSDeach time growing in the number of languages offered in the tasks and in the number of participating teams.

Beginning with the fourth workshop, SemEval SemEval-1the nature of the tasks evolved to include semantic analysis tasks outside of word sense disambiguation. It was also the decision that not every evaluation task will be run every year, e.

The early s saw the beginnings of more systematic and rigorous intrinsic evaluations, including more formal experimentation on small sets of ambiguous words. After SemEval, many participants feel that the 3-year cycle is a long wait. For this reason, the SemEval coordinators gave the opportunity for task organizers to choose between a 2-year or a 3-year cycle.

Although the votes within the SemEval community favored a 3-year cycle, organizers and coordinators had settled to split the SemEval task into 2 evaluation workshops. Thus was born SemEval and SemEval Senseval-3 looked beyond the lexemes and started to evaluate systems that looked into wider areas of semantics, such as Semantic Roles technically known as Theta roles in formal semanticsLogic Form Transformation commonly semantics of phrases, clauses or sentences were represented in first-order logic forms and Senseval-3 explored performances of semantics analysis on Machine translation.

As the types of different computational semantic systems grew beyond the coverage of WSD, Senseval evolved into SemEval, where more aspects of computational semantic systems were evaluated.

Cross Temporal Recurrent Networks for Ranking Question Answer Pairs

The SemEval exercises provide a mechanism for examining issues in semantic analysis of texts. The topics of interest fall short of the logical rigor that is found in formal computational semantics, attempting to identify and characterize the kinds of issues relevant to human understanding of language. The primary goal is to replicate human processing by means of computer systems. The tasks shown below are developed by individuals and groups to deal with identifiable issues, as they take on some concrete form.

The first major area in semantic analysis is the identification of the intended meaning at the word level taken to include idiomatic expressions. This is word-sense disambiguation a concept that is evolving away from the notion that words have discrete senses, but rather are characterized by the ways in which they are used, i.

The tasks in this area include lexical sample and all-word disambiguation, multi- and cross-lingual disambiguation, and lexical substitution. Given the difficulties of identifying word senses, other tasks relevant to this topic include word-sense induction, subcategorization acquisition, and evaluation of lexical resources.

The second major area in semantic analysis is the understanding of how different sentence and textual elements fit together.

Tasks in this area include semantic role labeling, semantic relation analysis, and coreference resolution. Other tasks in this area look at more specialized issues of semantic analysis, such as temporal information processing, metonymy resolution, and sentiment analysis.

The tasks in this area have many potential applications, such as information extraction, question answering, document summarization, machine translation, construction of thesauri and semantic networks, language modeling, paraphrasing, and recognizing textual entailment. In each of these potential applications, the contribution of the types of semantic analysis constitutes the most outstanding research issue. For example, in the word sense induction and disambiguation task, there are three separate phases:.

Unlike similar task like crosslingual WSD or the multilingual lexical substitution task, where no fixed sense inventory is specified, Multilingual WSD uses the BabelNet as its sense inventory. The task is an unsupervised Word Sense Disambiguation task for English nouns by means of parallel corpora.

It follows the lexical-sample variant of the Classic WSD task, restricted to only 20 polysemous nouns. The major tasks in semantic evaluation include the following areas of natural language processing. This list is expected to grow as the field progresses.Explore the words cloud of the CogNet project.

It provides you a very rough idea of what is the project "CogNet" about. Organization address address: Atir Yeda St. Current 4G technology is approaching the limits of what is possible with this generation of radio technology and to address this, one of the key requirements of 5G will be to create a network that is highly optimised to make maximum use of available radio spectrum and bandwidth for QoS, and because of the network size and number of devices connected, it will be necessary for it to largely manage itself and deal with organisation, configuration, security, and optimisation issues.

Virtualisation will also play an important role as the network will need to provision itself dynamically to meet changing demands for resources and Network Function Virtualisation NFVthe virtualising of network nodes functions and links, will be the key technology for this. We believe that Autonomic Network Management based on Machine Learning will be a key technology enabling an almost self administering and self managing network.

Network software will be capable of forecasting resource demand requirements through usage prediction, recognising error conditions, security conditions, outlier events such as fraud, and responding and taking corrective actions.

Energy efficiency will also be a key requirement with the possibility to reconfigure the NFV to for example avail of cheaper or greener energy when it is available and suitable. Again this is directly related to usage prediction both at a macro level, across an entire network, and at a micro level within specific cells.

The Cognet proposal will focus on applying Machine Learning research to these domains to enable the level of Network Management technology required to fulfil the 5G vision. Are you the coordinator or a participant of this project?

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For instance: the website url it has not provided by EU-opendata yetthe logo, a more detailed description of the project in plain text as a rtf file or a word filesome pictures as picture files, not embedded into any word filetwitter account, linkedin page, etc. And then put a link of this page into your project's website. Partnership 0. Views 0. Outcomes and results. CogNet project word cloud Explore the words cloud of the CogNet project.

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convkn at semeval

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convkn at semeval

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