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Found 50 papers, 16 papers with code
ViLBERTScore: Evaluating Image Caption Using Vision-and-Language BERT
In this paper, we propose an evaluation metric for image captioning systems using both image and text information.
Modality Alignment between Deep Representations for Effective Video-and-Language Learning
Our method directly optimizes CKA to make an alignment between video and text embedding representations, hence it aids the cross-modality attention module to combine information over different modalities.
Improving Multiple Documents Grounded Goal-Oriented Dialog Systems via Diverse Knowledge Enhanced Pretrained Language Model
In this paper, we mainly discuss about our submission to MultiDoc2Dial task, which aims to model the goal-oriented dialogues grounded in multiple documents.
Learning to Select Question-Relevant Relations for Visual Question Answering
Previous existing visual question answering (VQA) systems commonly use graph neural networks(GNNs) to extract visual relationships such as semantic relations or spatial relations.
Contrastive Learning for Context-aware Neural Machine Translation Using Coreference Information
Context-aware neural machine translation (NMT) incorporates contextual information of surrounding texts, that can improve the translation quality of document-level machine translation.
Asking Clarification Questions to Handle Ambiguity in Open-Domain QA
We first present CAMBIGNQ, a dataset consisting of 5, 654 ambiguous questions, each with relevant passages, possible answers, and a clarification question.
Target-Agnostic Gender-Aware Contrastive Learning for Mitigating Bias in Multilingual Machine Translation
Our method is target-language-agnostic and applicable to already trained multilingual machine translation models through post-fine-tuning.
Multi-View Zero-Shot Open Intent Induction from Dialogues: Multi Domain Batch and Proxy Gradient Transfer
In Task Oriented Dialogue (TOD) system, detecting and inducing new intents are two main challenges to apply the system in the real world.
PR-MCS: Perturbation Robust Metric for MultiLingual Image Captioning
Vulnerability to lexical perturbation is a critical weakness of automatic evaluation metrics for image captioning.
Varianceflow: High-Quality and Controllable Text-to-Speech using Variance Information via Normalizing Flow
Also, the objective function of NF makes the model use the variance information and the text in a disentangled manner resulting in more precise variance control.
Critic-Guided Decoding for Controlled Text Generation
In this work, we propose a novel critic decoding method for controlled language generation (CriticControl) that combines the strengths of reinforcement learning and weighted decoding.
Multimodal Speech Emotion Recognition using Cross Attention with Aligned Audio and Text
Then, the attention weights of each modality are applied directly to the other modality in a crossed way, so that the CAN gathers the audio and text information from the same time steps based on each modality.
Task-specific Compression for Multi-task Language Models using Attribution-based Pruning
However, these language models utilize an unnecessarily large number of model parameters, even when used only for a specific task.
Masked Summarization to Generate Factually Inconsistent Summaries for Improved Factual Consistency Checking
To this end, the latest approach is to train a factual consistency classifier on factually consistent and inconsistent summaries.
Factual Error Correction for Abstractive Summaries Using Entity Retrieval
In this paper, we propose an efficient factual error correction system RFEC based on entities retrieval post-editing process.
CrossAug: A Contrastive Data Augmentation Method for Debiasing Fact Verification Models
Specifically, we employ a two-stage augmentation pipeline to generate new claims and evidences from existing samples.
DPP-TTS: Diversifying prosodic features of speech via determinantal point processes
With the rapid advancement in deep generative models, recent neural text-to-speech models have succeeded in synthesizing human-like speech, even in an end-to-end manner.
Contrastive Learning for Context-aware Neural Machine TranslationUsing Coreference Information
We experimented with our method on common context-aware NMT models and two document-level translation tasks.
Self-Adapter at SemEval-2021 Task 10: Entropy-based Pseudo-Labeler for Source-free Domain Adaptation
Source-free domain adaptation is an emerging line of work in deep learning research since it is closely related to the real-world environment.
UMIC: An Unreferenced Metric for Image Captioning via Contrastive Learning
Also, we observe critical problems of the previous benchmark dataset (i. e., human annotations) on image captioning metric, and introduce a new collection of human annotations on the generated captions.
Neural Sequence-to-grid Module for Learning Symbolic Rules
Logical reasoning tasks over symbols, such as learning arithmetic operations and computer program evaluations, have become challenges to deep learning.
Bidirectional Variational Inference for Non-Autoregressive Text-to-Speech
Although early text-to-speech (TTS) models such as Tacotron 2 have succeeded in generating human-like speech, their autoregressive (AR) architectures have a limitation that they require a lot of time to generate a mel-spectrogram consisting of hundreds of steps.
Collaborative Training of GANs in Continuous and Discrete Spaces for Text Generation
Applying generative adversarial networks (GANs) to text-related tasks is challenging due to the discrete nature of language.
KPQA: A Metric for Generative Question Answering Using Keyphrase Weights
To evaluate our metric, we create high-quality human judgments of correctness on two GenQA datasets.
Fast and Accurate Deep Bidirectional Language Representations for Unsupervised Learning
Even though BERT achieves successful performance improvements in various supervised learning tasks, applying BERT for unsupervised tasks still holds a limitation that it requires repetitive inference for computing contextual language representations.
DSTC8-AVSD: Multimodal Semantic Transformer Network with Retrieval Style Word Generator
Audio Visual Scene-aware Dialog (AVSD) is the task of generating a response for a question with a given scene, video, audio, and the history of previous turns in the dialog.
Drug-disease Graph: Predicting Adverse Drug Reaction Signals via Graph Neural Network with Clinical Data
In this study, we develop a novel graph-based framework for ADR signal detection using healthcare claims data.
BaitWatcher: A lightweight web interface for the detection of incongruent news headlines
In digital environments where substantial amounts of information are shared online, news headlines play essential roles in the selection and diffusion of news articles.
Attentive Modality Hopping Mechanism for Speech Emotion Recognition
In this work, we explore the impact of visual modality in addition to speech and text for improving the accuracy of the emotion detection system.
Propagate-Selector: Detecting Supporting Sentences for Question Answering via Graph Neural Networks
In this study, we propose a novel graph neural network called propagate-selector (PS), which propagates information over sentences to understand information that cannot be inferred when considering sentences in isolation.
Surf at MEDIQA 2019: Improving Performance of Natural Language Inference in the Clinical Domain by Adopting Pre-trained Language Model
While deep learning techniques have shown promising results in many natural language processing (NLP) tasks, it has not been widely applied to the clinical domain.
MILAB at SemEval-2019 Task 3: Multi-View Turn-by-Turn Model for Context-Aware Sentiment Analysis
This paper describes our system for SemEval-2019 Task 3: EmoContext, which aims to predict the emotion of the third utterance considering two preceding utterances in a dialogue.
A Compare-Aggregate Model with Latent Clustering for Answer Selection
In this paper, we propose a novel method for a sentence-level answer-selection task that is a fundamental problem in natural language processing.
Ranked #7 on
Question Answering
on TrecQA
Effective Sentence Scoring Method using Bidirectional Language Model for Speech Recognition
Recent studies have tried to use bidirectional LMs (biLMs) instead of conventional unidirectional LMs (uniLMs) for rescoring the $N$-best list decoded from the acoustic model.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Speech Emotion Recognition Using Multi-hop Attention Mechanism
As opposed to using knowledge from both the modalities separately, we propose a framework to exploit acoustic information in tandem with lexical data.
Detecting Incongruity Between News Headline and Body Text via a Deep Hierarchical Encoder
Some news headlines mislead readers with overrated or false information, and identifying them in advance will better assist readers in choosing proper news stories to consume.
Multimodal Speech Emotion Recognition Using Audio and Text
Speech emotion recognition is a challenging task, and extensive reliance has been placed on models that use audio features in building well-performing classifiers.
Improving Neural Question Generation using Answer Separation
Previous NQG models suffer from a problem that a significant proportion of the generated questions include words in the question target, resulting in the generation of unintended questions.
Comparative Studies of Detecting Abusive Language on Twitter
However, this dataset has not been comprehensively studied to its potential.
Number Sequence Prediction Problems for Evaluating Computational Powers of Neural Networks
We define the complexity and difficulty of a number sequence prediction task with the structure of the smallest automaton that can generate the sequence.
AttnConvnet at SemEval-2018 Task 1: Attention-based Convolutional Neural Networks for Multi-label Emotion Classification
In this paper, we propose an attention-based classifier that predicts multiple emotions of a given sentence.
Learning to Rank Question-Answer Pairs using Hierarchical Recurrent Encoder with Latent Topic Clustering
In this paper, we propose a novel end-to-end neural architecture for ranking candidate answers, that adapts a hierarchical recurrent neural network and a latent topic clustering module.
Ranked #1 on
Answer Selection
on Ubuntu Dialogue (v1, Ranking)
Synonym Discovery with Etymology-based Word Embeddings
We test our model in the Chinese and Sino-Korean vocabularies.
Efficient Transfer Learning Schemes for Personalized Language Modeling using Recurrent Neural Network
In this paper, we propose an efficient transfer leaning methods for training a personalized language model using a recurrent neural network with long short-term memory architecture.
Partition-Merge: Distributed Inference and Modularity Optimization
This paper presents a novel meta algorithm, Partition-Merge (PM), which takes existing centralized algorithms for graph computation and makes them distributed and faster.
Efficient Energy Minimization for Enforcing Statistics
However, for many computer vision problems, the MAP solution under the model is not the ground truth solution.
Multi-dimensional Parametric Mincuts for Constrained MAP Inference
We show how this constrained discrete optimization problem can be formulated as a multi-dimensional parametric mincut problem via its Lagrangian dual, and prove that our algorithm isolates all constraint instances for which the problem can be solved exactly.
Local Rules for Global MAP: When Do They Work ?
We consider the question of computing Maximum A Posteriori (MAP) assignment in an arbitrary pair-wise Markov Random Field (MRF).
Local Algorithms for Approximate Inference in Minor-Excluded Graphs
We present a new local approximation algorithm for computing MAP and log-partition function for arbitrary exponential family distribution represented by a finite-valued pair-wise Markov random field (MRF), say G. Our algorithm is based on decomposing G into appropriately chosen small components; computing estimates locally in each of these components and then producing a good global solution.
