While aspect phrases extraction and sentiment analysis have received a lot of attention, clustering of aspect phrases and assigning human readable names to clusters in e-commerce reviews is an extremely important and challenging problem due to the scale of the reviews that makes human review infeasible.
The shared task addressed two of the challenges faced by medical video question answering: (I) a video classification task that explores new approaches to medical video understanding (labeling), and (ii) a visual answer localization task.
One of the cardinal tasks in achieving robust consumer health question answering systems is the question summarization and multi-document answer summarization.
Toward this, this paper is focused on answering health-related questions asked by the public by providing visual answers from medical videos.
Most of the existing methods primarily focus on lifelong learning within a static environment and lack the ability to mitigate forgetting in a quickly-changing dynamic environment.
We present Generalized LoRA (GLoRA), an advanced approach for universal parameter-efficient fine-tuning tasks.
In a popular e-commerce store, we have deployed our models for 1000s of (product-type, attribute) pairs.
Pre-trained language models (PLMs) have proven to be effective for document re-ranking task.
Many real-world applications based on online learning produce streaming data that is haphazard in nature, i. e., contains missing features, features becoming obsolete in time, the appearance of new features at later points in time and a lack of clarity on the total number of input features.
Text classification helps analyse texts for semantic meaning and relevance, by mapping the words against this hierarchy.
Out of this, the application that is making sure that the world stays in touch with each other and with current affairs is social media.
We extensively tested the proposed NNS approach and compared the performance with state-of-the-art NNS approaches on benchmark datasets and our created medical image datasets.
The quest for seeking health information has swamped the web with consumers' health-related questions.
This paper introduces a new challenge and datasets to foster research toward designing systems that can understand medical videos and provide visual answers to natural language questions.
We also create the large-scale multilingual and code-mixed VQA dataset in eleven different language setups considering the multiple Indian and European languages.
For the optimization of educational programs, it is crucial to design course learning outcomes (CLOs) according to the different cognitive levels of Bloom Taxonomy.
The growth of online consumer health questions has led to the necessity for reliable and accurate question answering systems.
In this paper, we study the task of abstractive summarization for real-world consumer health questions.
This has led to the introduction of a variety of deep learning systems and studies have shown that the accuracy of COVID-19 patient detection through the use of chest X-rays is strongly optimistic.
In this paper, we propose a hybrid technique for semantic question matching.
We show that small base networks when rescaled, can provide performance comparable to deeper networks with as low as 6% of optimization parameters of the deeper one.
In contrast to the usual setting where the particle is instantaneously reset to a preferred location (say, the origin), here we consider a finite time resetting process facilitated by an external linear potential $V(x)=\lambda|x|~ (\lambda>0)$.
We propose a robust tech- nique capable of handling the multilingual and code-mixed question to provide the answer against the visual information (image).
Classically, visual object tracking involves following a target object throughout a given video, and it provides us the motion trajectory of the object.
Code-mixing, the interleaving of two or more languages within a sentence or discourse is ubiquitous in multilingual societies.
To address this issue, we propose a hierarchical deep multi-modal network that analyzes and classifies end-user questions/queries and then incorporates a query-specific approach for answer prediction.
Companies are eager to have a smart supply chain especially when they have a dynamic system.
Question generation (QG) attempts to solve the inverse of question answering (QA) problem by generating a natural language question given a document and an answer.
Modified VGG (MVGG), residual network, mobile network is proposed and implemented in this paper.
In this paper, we propose a method for incorporating world knowledge (linked entities and fine-grained entity types) into a neural question generation model.
We evaluate the performance of our proposed model on a benchmark customer review dataset, comprising of the reviews of Hotel and Electronics domains.
In this paper, we propose a linguistically motivated technique for code-mixed question generation (CMQG) and a neural network based architecture for code-mixed question answering (CMQA).
Answer triggering is the task of selecting the best-suited answer for a given question from a set of candidate answers if exists.
Our empirical analysis shows that our models perform well in all the four languages on the setups of IJCNLP Shared Task on Customer Feedback Analysis.
Analyzing customer feedback is the best way to channelize the data into new marketing strategies that benefit entrepreneurs as well as customers.
In this paper we present the system for Answer Selection and Ranking in Community Question Answering, which we build as part of our participation in SemEval-2017 Task 3.
For the task of PoS tagging on Code-Mixed Indian Social Media Text, we develop a supervised system based on Conditional Random Field classifier.