A Task-Aware Empirical Know-How Map Built on Domain Q&A.

Extensive experiments on a comprehensive collection of benchmark datasets verify the effectiveness of our task-aware specialization strategy, where we outperform state-of-the-art scientific pre-trained ... To bridge this gap, we propose a multi-task contrastive learning framework, SciMult, with a focus on facilitating common knowledge sharing across different scientific literature understanding tasks while ... As far as we know, this is a pioneering study that explores the effect of MoE and instructions in the scientific domain. ...

arXiv:2305.14232v2 fatcat:isu6ehdtijc7jkkhjhfbcymsme

Multiple Versions

This paper presents a follow-up work of our network analysis on the myExperiment, an online scientific workflow repository. ... Two approaches are proposed over the ServiceMap: association rule mining and relation-aware, crossworkflow searching. ... Because he is new to the services, the scientist intends to know how his peer scientists use them together with other services of which he may or may not be aware. ...

doi:10.1109/scc.2011.122 dblp:conf/IEEEscc/TanZMFRG11 fatcat:wmsckksznjdgtf7b6lgj7syoyq

Then, we show how one can implement a task-based bit-constrained MIMO receiver, presenting approaches ranging from conventional hybrid receiver architectures to structures exploiting the dynamic nature ... In this work we survey the theory and design approaches to task-based quantization, presenting model-aware designs as well as data-driven implementations. ... MODEL-AWARE TASK-BASED QUANTIZATION In this section we detail how to design hybrid quantization systems to facilitate the recovery of the task vector s in the digital domain, based on prior knowledge of ...

arXiv:2002.04290v1 fatcat:ar7e7xo3kjdxrjh55vilp3irg4

We measure the semantic complexity of understanding of prepositional phrases, of an "in depth understanding system", and of a natural language interface to an on-line calendar. ... We define semantic complexity using a new concept of meaning automata. ... Savitch for comments on an earlier draft. ...

arXiv:cmp-lg/9505019v1 fatcat:jvlebbl5z5cavbkabsw3bb722y

The contribution of this paper is a framework for quantifying term frequency (TF) variants with sentiments. ... Sentiment analysis has received much attention in Information Retrieval (IR) and other domains including data mining, machine learning algorithms and NLP. ... These models are built upon VADER scores. Based on the VADER lexicon, a lexical feature could have a score between +1 and -1. ...

doi:10.3233/faia210228 dblp:conf/mmbd/BahraniR21 fatcat:ko7dd3qowrecvabbo6dzjy3isu

A methodology (based on causal mapping, self-Q and storytelling) for empirically researching the subject is outlined. ... However, there is little empirical research to support this theoretical proposition. Tacit knowledge has so far resisted operationalization. ... term "know-how" ' (p. 98). ...

doi:10.1111/1467-6486.00260 fatcat:csuos3atq5dfteo5lirixhtmne

Existing open-domain human-computer conversation systems are typically passive: they either synthesize or retrieve a reply provided a human-issued utterance. ... We design a pipeline to determine when, what, and how to introduce new content during human-computer conversation. ... In this way, our system is well aware of when, what, and how to proactively introduce new context in a continuous human-computer conversation. ...

arXiv:1604.04358v1 fatcat:gdmb7eh55nbsdcc5inlregfp2i

Secondly, we discuss a method of transferring RL policies from simulation to reality in order to make the agent experience situations in simulation, so it knows how to react to them in reality. ... Reinforcement learning (RL) is a booming area in artificial intelligence. ... This way new knowledge is built on something they already know, minimising the amount of new material needed to learn. ...

doi:10.3390/s21237829 pmid:34883832 fatcat:6hbyyucv7fehdhri7kxymadtfm

DOAJ

Such a learning approach may be useful when the performance of the task depends on solving a large amount of classification problems and each has its own characteristics which may or may not fit a particular ... The task of semantic parser acquisition in two different domains was attempted and preliminary results demonstrated that such an approach is promising. ... The only exceptions of which we are aware are a statistical approach to mapping airline-information queries into SQL presented in [10] , a probabilistic decision-tree method for the same task described ...

doi:10.1007/3-540-44795-4_40 fatcat:kyho77vr4fgn5cvwxvn7jqel2e

Our work is based on two key ideas. The first is that of using domains, i.e. a general semantic-aware methodology and technique for structuring the background knowledge. ... Dealing with this problem has turned out to be a very difficult task because on the one hand the background knowledge should be very large and virtually unbound and, on the other hand, it should be context ... Each F NL : S NL → {C  E  Q  q} is a mapping function that for each synset s  S NL in a natural language NL assigns an element in C  E  Q  q. ...

doi:10.1016/j.websem.2011.11.007 fatcat:byeefjjw4fd3dovftt5z3sgvqi

Szczepanski

We illustrate the sort of analysis we favor with a scenario from emergency management, and consider implications and future directions for system design and empirical methods. q Interacting with Computers ... A contemporary approach to describing and theorizing about joint human endeavor is to posit 'knowledge in common' as a basis for awareness and coordination. ... The key idea is to descriptively isolate task-relevant knowledge shared by all team members-knowledge about task relevant objects, knowledge of how to carry out domain procedures, knowledge about domain ...

doi:10.1016/j.intcom.2005.05.005 fatcat:67k4bkvecfdjjcec62pgkych7m

Experimental results on four translation tasks demonstrate that the proposed method not only carries out translation tasks intact but also delivers outstanding performance on quality estimation. ... To fill this gap, we propose a novel competency-aware NMT by extending conventional NMT with a self-estimator, offering abilities to translate a source sentence and estimate its competency. ... On the contrary, our method performs better across out-of-domain tests due to its competency awareness. Even if the domain has not been learned by CANMT, it still knows its incapability. ...

arXiv:2211.13865v1 fatcat:4xw7aokfefgwvjtlqqsao6hovy

Open Access

Thereafter, we develop a probabilistic model for suggesting the suitable treatments or procedures for a particular disease or health condition. ... For a blog classified with positive sentiment, we retrieve the top-n similar posts. ... The medical domain itself is sensitive to misinformation. Thus, any system built on this data would also have to incorporate relevant domain knowledge. ...

arXiv:1909.08686v1 fatcat:uvsbjhr64rf3tppdxy6an5fx4i

The system adopts a data-driven approach to suggest semantically relevant and context-aware queries for application domains of users' interest based on their query logs. ... It makes them unable to systematically elicit a series of topically-related and meaningful queries for insight discovery in target domains. ... Articulate [2] is a more intelligent NLI that first maps NL queries to some analytical tasks and then decides proper visual encodings based on the tasks and data characteristics. ...

arXiv:2201.04868v2 fatcat:vplbllcv3vdb5nlq7p77vf6jtu

Multiple Versions

The empirical study shows that MRNN with the duplex attention is significantly superior to existing models used for ad-hoc retrieval on benchmark datasets including SQuAD, WikiQA, QUASAR, and TrecQA. ... It requires a good understanding of the query and all the documents in a corpus, which is difficult because the meaning of natural language texts depends on the context, syntax,and semantics. ... DrQA [13] is built on top of two component; a Document Retriever and a Document Reader respectively. The Document Retriever is a TF-IDF [2] retrieval system built upon Wikipedia corpus. ...

arXiv:1911.00964v1 fatcat:vxhhe4fbwrhcxbgdit36mwomqy

Open Access

Pre-training Multi-task Contrastive Learning Models for Scientific Literature Understanding [article]

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ServiceMap: Providing Map and GPS Assistance to Service Composition in Bioinformatics

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Task-Based Quantization with Application to MIMO Receivers [article]

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Measuring semantic complexity [article]

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Opinion-Aware Retrieval Models Based on Sentiment and Intensity of Lexical Features

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Tacit Knowledge: Some Suggestions for Operationalization

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StalemateBreaker: A Proactive Content-Introducing Approach to Automatic Human-Computer Conversation [article]

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Overcoming Challenges of Applying Reinforcement Learning for Intelligent Vehicle Control

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Using Multiple Clause Constructors in Inductive Logic Programming for Semantic Parsing [chapter]

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Domains and context: First steps towards managing diversity in knowledge

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Awareness and teamwork in computer-supported collaborations

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Competency-Aware Neural Machine Translation: Can Machine Translation Know its Own Translation Quality? [article]

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Sentiment-Aware Recommendation System for Healthcare using Social Media [article]

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Interactive Data Analysis with Next-step Natural Language Query Recommendation [article]

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Other Versions

MRNN: A Multi-Resolution Neural Network with Duplex Attention for Document Retrieval in the Context of Question Answering [article]

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