However, the spread impact of emotions in a conversation is rarely addressed in existing researches.
Ranked #3 on Emotion Recognition in Conversation on MELD
Neural passage retrieval is a new and promising approach in open retrieval question answering.
Deep learning models exhibit a preference for statistical fitting over logical reasoning.
In this paper, we propose a Graph Pattern Loss based Diversified Attention Network(GPLDAN) for unsupervised cross-modal retrieval to deeply analyze correlations among representations.
Extensive experiments on two real-world tasks show that AdvGraph exhibits better performance compared with previous work: (i) effective - it significantly strengthens the model robustness even under the adaptive attacks setting without negative impact on model performance over legitimate input; (ii) generic - its key component, i. e., the representation of connotative adversarial knowledge is task-agnostic, which can be reused in any Chinese-based NLP models without retraining; and (iii) efficient - it is a light-weight defense with sub-linear computational complexity, which can guarantee the efficiency required in practical scenarios.
To the best of our knowledge, it is the largest dataset for brand detection and recognition with rich annotations.
End-to-end question answering (QA) requires both information retrieval (IR) over a large document collection and machine reading comprehension (MRC) on the retrieved passages.
2) There is no previous work considering adversarial attack to improve the performance of NLSM tasks.
Answer validation in machine reading comprehension (MRC) consists of verifying an extracted answer against an input context and question pair.
Transfer learning techniques are particularly useful in NLP tasks where a sizable amount of high-quality annotated data is difficult to obtain.
To this end, this paper proposes an end-to-end method to solve the task of Pair-wise Aspect and Opinion Terms Extraction (PAOTE).
We firstly propose to explore the asking emphasis of a question as a key factor in DQI.
Ranked #1 on Community Question Answering on Quora Question Pairs
In this paper, we revisit systematic generalization from the perspective of meaningful learning, an exceptional capability of humans to learn new concepts by connecting them with other previously known knowledge.
To deal with shadows, we build up an autonomous driving shadow dataset and design a deep neural network to detect shadows automatically.
1 code implementation • • Vittorio Castelli, Rishav Chakravarti, Saswati Dana, Anthony Ferritto, Radu Florian, Martin Franz, Dinesh Garg, Dinesh Khandelwal, Scott McCarley, Mike McCawley, Mohamed Nasr, Lin Pan, Cezar Pendus, John Pitrelli, Saurabh Pujar, Salim Roukos, Andrzej Sakrajda, Avirup Sil, Rosario Uceda-Sosa, Todd Ward, Rong Zhang
We introduce TechQA, a domain-adaptation question answering dataset for the technical support domain.
With increasing popularity in online learning, a surge of E-learning platforms have emerged to facilitate education opportunities for k-12 (from kindergarten to 12th grade) students and with this, a wealth of information on their learning logs are getting recorded.
However, the video length is usually long, and there are hierarchical relationships between frames across events in the video, the performance of RNN based models are decreased.
Ranked #1 on Video Classification on YouTube-8M
1 code implementation • 23 Jan 2019 • Wei Li, Chengwei Pan, Rong Zhang, Jiaping Ren, Yuexin Ma, Jin Fang, Feilong Yan, Qichuan Geng, Xinyu Huang, Huajun Gong, Weiwei Xu, Guoping Wang, Dinesh Manocha, Ruigang Yang
Our augmented approach combines the flexibility in a virtual environment (e. g., vehicle movements) with the richness of the real world to allow effective simulation of anywhere on earth.
Deep neural network models have recently draw lots of attention, as it consistently produce impressive results in many computer vision tasks such as image classification, object detection, etc.
The practice in an elderly-care company shows that the FPQM can reduce the number of attributes by 90. 56% with a prediction accuracy of 98. 39%.