Search Results for author:
Found 16 papers, 8 papers with code
EBBS: An Ensemble with Bi-Level Beam Search for Zero-Shot Machine Translation
The ability of zero-shot translation emerges when we train a multilingual model with certain translation directions; the model can then directly translate in unseen directions.
Learning Non-Autoregressive Models from Search for Unsupervised Sentence Summarization
Text summarization aims to generate a short summary for an input text.
Switch as a Verifier: Toward Scalable Data Plane Checking via Distributed, On-Device Verification
Data plane verification (DPV) is important for finding network errors.
Non-Autoregressive Translation with Layer-Wise Prediction and Deep Supervision
How do we perform efficient inference while retaining high translation quality?
Simulated Annealing for Emotional Dialogue Systems
Explicitly modeling emotions in dialogue generation has important applications, such as building empathetic personal companions.
A Globally Normalized Neural Model for Semantic Parsing
In this paper, we propose a globally normalized model for context-free grammar (CFG)-based semantic parsing.
Seq2Emo: A Sequence to Multi-Label Emotion Classification Model
Multi-label emotion classification is an important task in NLP and is essential to many applications.
Basic and Depression Specific Emotion Identification in Tweets: Multi-label Classification Experiments
In this paper, we present empirical analysis on basic and depression specific multi-emotion mining in Tweets with the help of state of the art multi-label classifiers.
Boundary-Aware Segmentation Network for Mobile and Web Applications
In this paper, we propose a simple yet powerful Boundary-Aware Segmentation Network (BASNet), which comprises a predict-refine architecture and a hybrid loss, for highly accurate image segmentation.
Optimizing Deeper Transformers on Small Datasets
This work shows that this does not always need to be the case: with proper initialization and optimization, the benefits of very deep transformers can carry over to challenging tasks with small datasets, including Text-to-SQL semantic parsing and logical reading comprehension.
ANA at SemEval-2020 Task 4: mUlti-task learNIng for cOmmonsense reasoNing (UNION)
In order to generate more meaningful explanations, we propose UNION, a unified end-to-end framework, to utilize several existing commonsense datasets so that it allows a model to learn more dynamics under the scope of commonsense reasoning.
U$^2$-Net: Going Deeper with Nested U-Structure for Salient Object Detection
In this paper, we design a simple yet powerful deep network architecture, U$^2$-Net, for salient object detection (SOD).
Ranked #1 on
Salient Object Detection
on SOD
Seq2Emo for Multi-label Emotion Classification Based on Latent Variable Chains Transformation
Most of the existing methods treat this task as a problem of single-label multi-class text classification.
ANA at SemEval-2019 Task 3: Contextual Emotion detection in Conversations through hierarchical LSTMs and BERT
This paper describes the system submitted by ANA Team for the SemEval-2019 Task 3: EmoContext.
Ranked #3 on
Emotion Recognition in Conversation
on EC
Emotion Recognition in Conversation
General Classification
+2
Generating Responses Expressing Emotion in an Open-domain Dialogue System
Neural network-based Open-ended conversational agents automatically generate responses based on predictive models learned from a large number of pairs of utterances.
Automatic Dialogue Generation with Expressed Emotions
Despite myriad efforts in the literature designing neural dialogue generation systems in recent years, very few consider putting restrictions on the response itself.
