Papers by Trung Bui
Rethinking Self-Attention: Towards Interpretability in Neural Parsing (2020.findings-emnlp)
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| Challenge: | Recent work shows that attention mechanisms provide arguably explainable attention distributions that can help to interpret predictions. |
| Approach: | They propose a new self-attention layer where attention heads represent labels. |
| Outcome: | The proposed model obtains state-of-the-art results on the Penn Treebank and Chinese Treebank. |
Multimodal Intent Discovery from Livestream Videos (2022.findings-naacl)
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Adyasha Maharana, Quan Tran, Franck Dernoncourt, Seunghyun Yoon, Trung Bui, Walter Chang, Mohit Bansal
| Challenge: | Existing models for instructional video understanding struggle to understand abstract intents . identifying procedural intent within instructional videos is a challenging task . |
| Approach: | They propose to extract instructional intent from software instructional livestreams by using a multimodal cascaded cross-attention model that integrates weaker and noisier video signals with more discriminative text signals. |
| Outcome: | The proposed model improves on baseline models and compares it to existing models. |
The Context-Dependent Additive Recurrent Neural Net (N18-1)
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| Challenge: | Contextual sequence mapping is one of the fundamental problems in Natural Language Processing (NLP). |
| Approach: | They propose a new family of Recurrent Neural Networks that address contextual sequence mapping . they propose to use contextual signals to control the flow of information . |
| Outcome: | The proposed architecture outperforms existing methods on dialog problem and language model . the proposed architectures are based on a novel family of recurrent neural networks . |
Propagate-Selector: Detecting Supporting Sentences for Question Answering via Graph Neural Networks (2020.lrec-1)
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| Challenge: | Existing question-answering models do not require reasoning across sentences in the given context (passage). |
| Approach: | They propose a graph neural network that propagates information over sentences to understand information that cannot be inferred when considering sentences in isolation. |
| Outcome: | The proposed approach obtains the best performance compared to the widely used answer-selection models that do not consider the intersentential relationship. |
Double Trouble: How to not Explain a Text Classifier’s Decisions Using Counterfactuals Synthesized by Masked Language Models? (2022.aacl-main)
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| Challenge: | Input Marginalization (IM) is a method that takes the prediction difference between before-and-after an input feature (here, a token) is removed as its attribution. |
| Approach: | They propose to use a BERT-based method to replace a token with a feature to give more plausible counterfactuals. |
| Outcome: | The proposed method is effective, but the Deletion-BERT metric is biased towards IM, and the results are not convincing. |
MeetingQA: Extractive Question-Answering on Meeting Transcripts (2023.acl-long)
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| Challenge: | Meeting transcripts are a promising domain for natural language tasks . lack of annotated data impedes research on other important tasks in this domain . |
| Approach: | They propose an extractive QA dataset comprising questions asked by meeting participants and corresponding responses. |
| Outcome: | The proposed dataset extracts questions asked by meeting participants and corresponding responses from transcripts. |
A Survey on LLM-based Conversational User Simulation (2026.eacl-long)
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Bo Ni, Yu Wang, Leyao Wang, Branislav Kveton, Franck Dernoncourt, Yu Xia, Hongjie Chen, Reuben Luera, Samyadeep Basu, Subhojyoti Mukherjee, Puneet Mathur, Nesreen K. Ahmed, Junda Wu, Li Li, Huixin Zhang, Ruiyi Zhang, Tong Yu, Sungchul Kim, Jiuxiang Gu, Zhengzhong Tu, Alexa Siu, Zichao Wang, Seunghyun Yoon, Nedim Lipka, Namyong Park, Zihao Lin, Trung Bui, Yue Zhao, Tyler Derr, Ryan A. Rossi
| Challenge: | Recent advances in large language models (LLMs) have enabled high-fidelity generation of synthetic user conversation. |
| Approach: | They propose a taxonomy covering user granularity and simulation objectives . they analyze core techniques and evaluation methodologies to help them understand the latest developments . |
| Outcome: | The proposed model enables high-fidelity generation of synthetic user conversation. |
Medical Question Understanding and Answering with Knowledge Grounding and Semantic Self-Supervision (2022.coling-1)
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Khalil Mrini, Harpreet Singh, Franck Dernoncourt, Seunghyun Yoon, Trung Bui, Walter W. Chang, Emilia Farcas, Ndapa Nakashole
| Challenge: | Current medical question answering systems have difficulty processing long, detailed and informally worded questions . a growing number of approaches attempt to enhance the processing of consumer health questions - or medical question understanding . |
| Approach: | They propose a medical question understanding and answering system with knowledge grounding and semantic self-supervision that matches a user question with a trusted medical knowledge base and retrieves a fixed number of relevant sentences from the corresponding answer document. |
| Outcome: | The proposed system retrieves more relevant answers while achieving 20 times faster. |
UMIC: An Unreferenced Metric for Image Captioning via Contrastive Learning (2021.acl-short)
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| Challenge: | BERTScore and other text generation metrics do not use reference captions to evaluate image captions. |
| Approach: | They propose a new metric which does not require reference captions to evaluate image captions . they train UMIC to discriminate negative captions via contrastive learning . |
| Outcome: | The proposed metric has higher correlation than previous metrics that require multiple references. |
A Review on Deep Learning Techniques Applied to Answer Selection (C18-1)
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| Challenge: | Existing deep learning methods for answer selection are not feature engineering or expensive external resources. |
| Approach: | They propose to use deep learning methods to analyze and predict answer quality . they use a set of candidate answers to identify which of the candidates answers the question correctly. |
| Outcome: | The proposed methods produce impressive performance without feature engineering or expensive external resources. |
PR-MCS: Perturbation Robust Metric for MultiLingual Image Captioning (2023.findings-emnlp)
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| Challenge: | Existing image captioning metrics are vulnerable to lexical perturbations, but they are not robust to such perturbations. |
| Approach: | They propose a perturbation-robust multilingual CLIPScore which is a reference-free image captioning metric for multiple languages. |
| Outcome: | The proposed metric outperforms baseline metrics in capturing lexical noise of all various perturbation types in all five languages while maintaining a strong correlation with human judgments. |
Virtual Knowledge Graph Construction for Zero-Shot Domain-Specific Document Retrieval (2022.coling-1)
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| Challenge: | Domain-specific documents cover terminologies and specialized knowledge. |
| Approach: | They propose a domain-specific document retrieval method that embeds a document into a graph of entities and their relations into . they compare the unsupervised method with previous approaches and use it to compute relevance between queries and documents. |
| Outcome: | The proposed method outperforms baselines and fully-supervised bi-encoders in a zero-shot setting and outperformed bi-supervised approaches. |
PiC: A Phrase-in-Context Dataset for Phrase Understanding and Semantic Search (2023.eacl-main)
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| Challenge: | Existing benchmarks for phrase-similarity compare phrases alone (without context) and phrases with context (with or without context). |
| Approach: | They propose to use a dataset of 28K noun phrases accompanied by their contextual Wikipedia pages to train machine phrase embeddings. |
| Outcome: | The proposed dataset improves ranking-models’ accuracy and pushes span selection models near human accuracy, which is 95% Exact Match (EM) on semantic search given a query phrase and a passage. |
KPQA: A Metric for Generative Question Answering Using Keyphrase Weights (2021.naacl-main)
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| Challenge: | Existing n-gram similarity metrics fail to discriminate the incorrect answers due to the free-form of the answer. |
| Approach: | They propose a new metric that assigns different weights to each token via keyphrase prediction to judge the correctness of GenQA. |
| Outcome: | The proposed metric has a significantly higher correlation with human judgments than existing metrics in various datasets. |
Can Large Language Models Keep Up? Benchmarking Online Adaptation to Continual Knowledge Streams (2026.acl-long)
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Jiyeon Kim, Hyunji Lee, Dylan Zhou, Sue Hyun Park, Seunghyun Yoon, Trung Bui, Franck Dernoncourt, Sungmin Cha, Minjoon Seo
| Challenge: | Existing models and agentic memory systems fail to adapt robustly to OAKS, demonstrating delays in state-tracking and susceptibility to distraction within streaming environments. |
| Approach: | They propose a benchmark to evaluate models' ability to adapt to changing knowledge over streaming . they use two datasets to analyze how facts evolve over time . |
| Outcome: | The proposed benchmark evaluates models in an online adaptation setting over streaming, continually updating knowledge. |
Keyphrase Prediction from Video Transcripts: New Dataset and Directions (2022.coling-1)
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Amir Pouran Ben Veyseh, Quan Hung Tran, Seunghyun Yoon, Varun Manjunatha, Hanieh Deilamsalehy, Rajiv Jain, Trung Bui, Walter W. Chang, Franck Dernoncourt, Thien Huu Nguyen
| Challenge: | Existing studies on keyphrase prediction have focused on formal texts and informal-text domains. |
| Approach: | They propose to annotate large-scale video transcripts with keyphrases from live-stream video . they propose to feed models with paragraph-level keyphrase extraction to foster future research . |
| Outcome: | The proposed model improves keyphrase prediction in live-stream video transcripts by feeding models with paragraph-level keyphrases. |
Open-Domain Question Answering with Pre-Constructed Question Spaces (2021.naacl-srw)
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| Challenge: | Open-domain question answering aims at locating answers to user-generated questions in massive collections of documents. |
| Approach: | They propose an algorithm with a novel reader-retriever design that differs from both families of algorithms. |
| Outcome: | The proposed algorithm outperforms retrieval-based methods with two large-scale datasets and is state-of-the-art. |
Lizard: An Efficient Linearization Framework for Large Language Models (2026.acl-long)
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Chien Van Nguyen, Huy Huu Nguyen, Ruiyi Zhang, Hanieh Deilamsalehy, Puneet Mathur, Viet Dac Lai, Haoliang Wang, Jayakumar Subramanian, Ryan A. Rossi, Trung Bui, Nikos Vlassis, Franck Dernoncourt, Thien Huu Nguyen
| Challenge: | Existing linearization frameworks that rely on softmax attention with quadratic time and memory complexity pose significant computational and memory bottlenecks for long-context applications. |
| Approach: | They propose a linearization framework that transforms pretrained Transformer-based Large Language Models into subquadratic architectures that closely approximate softmax attention while preserving model quality. |
| Outcome: | Experiments show that the proposed framework outperforms existing methods by 9.4 - 24.5 points on the 5-shot MMLU benchmark and demonstrating superior associative recall. |
Towards Enhancing Coherence in Extractive Summarization: Dataset and Experiments with LLMs (2024.emnlp-main)
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| Challenge: | Existing methods for extractive summarization lack coherence, despite improvements . a human-annotated dataset is used to improve coherency of extractive summary . |
| Approach: | They propose to use human-annotated datasets to create coherent extractive summaries . they use supervised fine-tuning and natural language user feedback to enhance coherence . |
| Outcome: | The proposed dataset shows that LLMs can produce coherent summaries with human feedback. |
A Discourse-Aware Attention Model for Abstractive Summarization of Long Documents (N18-2)
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Arman Cohan, Franck Dernoncourt, Doo Soon Kim, Trung Bui, Seokhwan Kim, Walter Chang, Nazli Goharian
| Challenge: | Existing abstractive summarization models focus on summarizing sentences and short documents. |
| Approach: | They propose a hierarchical encoder that models the discourse structure of a document, and an attentive discourse-aware decoder to generate the summary. |
| Outcome: | The proposed model significantly outperforms state-of-the-art models on two large-scale datasets of scientific papers. |
SCCS: Semantics-Consistent Cross-domain Summarization via Optimal Transport Alignment (2023.findings-acl)
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Jielin Qiu, Jiacheng Zhu, Mengdi Xu, Franck Dernoncourt, Trung Bui, Zhaowen Wang, Bo Li, Ding Zhao, Hailin Jin
| Challenge: | Existing methods for multimodal summarization ignore the structure and semantics of the whole video and article. |
| Approach: | They propose a semantic-consistent cross-domain summarization model that extracts features from video and article and uses fusion methods to select representative one. |
| Outcome: | The proposed model produces high-quality multimodal summaries on three MSMO datasets. |
Few-Shot Intent Detection via Contrastive Pre-Training and Fine-Tuning (2021.emnlp-main)
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Jianguo Zhang, Trung Bui, Seunghyun Yoon, Xiang Chen, Zhiwei Liu, Congying Xia, Quan Hung Tran, Walter Chang, Philip Yu
| Challenge: | Existing methods address few-shot intent detection tasks from two perspectives: data augmentation and task-adaptive training with pre-trained models. |
| Approach: | They propose a few-shot intent detection schema using contrastive pre-training and fine-tuning. |
| Outcome: | The proposed method achieves state-of-the-art performance on three challenging intent detection datasets under 5-shot and 10-shot settings. |
A Joint Learning Approach based on Self-Distillation for Keyphrase Extraction from Scientific Documents (2020.coling-main)
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| Challenge: | Existing methods for keyphrase extraction are limited by the number of annotated documents. |
| Approach: | They propose a joint learning approach that uses the idea of self-distillation to extract keyphrases from unlabeled articles. |
| Outcome: | The proposed approach outperforms baseline models on two public benchmarks: Inspec and SemEval-2017. |
ISA: An Intelligent Shopping Assistant (2020.aacl-demo)
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| Challenge: | In-store users only need to take a picture or scan the barcode of the product of interest, and then the user can talk to the assistant about the product. |
| Approach: | They present a mobile-based intelligent shopping assistant that is designed to improve shopping experience in physical stores. |
| Outcome: | The proposed system can improve shopping experience in physical stores by leveraging advanced techniques in computer vision, speech processing, and natural language processing. |
PEEB: Part-based Image Classifiers with an Explainable and Editable Language Bottleneck (2024.findings-naacl)
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| Challenge: | CLIP-based classifiers rely on the prompt containing a class name that is known to the text encoder and perform poorly on new classes or the classes whose names rarely appear on the Internet. |
| Approach: | They propose to use a set of text descriptors to express a class name into a textual descriptable and match the embeddings of the detected parts to their textual ones to compute a logit score. |
| Outcome: | The proposed classifier outperforms CLIP-based classifiers on zero-shot and supervised learning settings by 88.80% and 92.20% accuracy on CUB-200 and Stanford Dogs-120. |
Expressing Visual Relationships via Language (P19-1)
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| Challenge: | Current studies on image captioning focus on single image, but there are no effective models for generating relational captions for two images. |
| Approach: | They propose a language-guided image editing dataset that contains real image pairs with corresponding editing instructions. |
| Outcome: | The proposed model outperforms baseline and existing methods on two datasets. |
CORG: Generating Answers from Complex, Interrelated Contexts (2025.naacl-long)
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| Challenge: | Existing approaches to analyzing knowledge in a corpus often focus on single factors in isolation. |
| Approach: | They propose a framework that organizes multiple contexts into independently processed groups . they classify these relationships into distracting, ambiguous, counterfactual, and duplicated . |
| Outcome: | The proposed framework outperforms existing grouping methods and single-context approaches. |
Out of Order: How important is the sequential order of words in a sentence in Natural Language Understanding tasks? (2021.findings-acl)
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| Challenge: | In July 2019, RoBERTa was the first to surpass a human baseline on GLUE . since then, 13 more methods have outperformed humans on the GLu leaderboard . |
| Approach: | They found that 75% to 90% of correct predictions of BERT-based classifiers remain constant after input words are randomly shuffled. |
| Outcome: | The proposed model outperforms humans on GLUE and SQuAD 2.0. |
StreamHover: Livestream Transcript Summarization and Annotation (2021.emnlp-main)
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Sangwoo Cho, Franck Dernoncourt, Tim Ganter, Trung Bui, Nedim Lipka, Walter Chang, Hailin Jin, Jonathan Brandt, Hassan Foroosh, Fei Liu
| Challenge: | StreamHover is a framework for annotating and summarizing livestream transcripts . the problem is that there is n't enough annotated datasets to summarize livestreams based on the informal nature of spoken language . |
| Approach: | They propose a framework for annotating and summarizing livestream transcripts using a text preview. |
| Outcome: | The proposed model generalizes better and improves over strong baselines. |
Adjusting Image Attributes of Localized Regions with Low-level Dialogue (2020.lrec-1)
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| Challenge: | Image editing is time-consuming and requires a wide assortment of features and combinations of these features to achieve a desired effect. |
| Approach: | They propose a task-oriented dialogue system to investigate low-level instructions for NLIE . 25% of users found the system easy-to-use, resonating with their motivation . |
| Outcome: | The proposed system is easy-to-use and user-friendly. |
FIFA: Unified Faithfulness Evaluation Framework for Text-to-Video and Video-to-Text Generation (2026.findings-acl)
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| Challenge: | Existing evaluation methods for VideoMLLMs are limited to one task and fail to assess hallucinations in open-ended, free-form responses. |
| Approach: | They propose a unified framework that extracts comprehensive descriptive facts and models their semantic dependencies via a Spatio-Temporal Semantic Dependency Graph. |
| Outcome: | The proposed framework aligns more closely with human judgment than existing evaluation methods and improves factual consistency in both text and video generation. |
ChatGPT Beyond English: Towards a Comprehensive Evaluation of Large Language Models in Multilingual Learning (2023.findings-emnlp)
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| Challenge: | Recent advances in natural language processing (NLP) have led to significant breakthroughs in the field. |
| Approach: | They evaluate ChatGPT over multiple tasks with diverse languages and large datasets to provide more comprehensive information for multilingual NLP applications. |
| Outcome: | The proposed model can process and generate texts for multiple languages due to its multilingual training data. |
AutoNLU: An On-demand Cloud-based Natural Language Understanding System for Enterprises (2020.aacl-demo)
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| Challenge: | AutoNLU is an on-demand cloud-based system that enables users to create and edit datasets and train and test different state-of-the-art NLU models. |
| Approach: | They introduce an on-demand cloud-based system that provides an easy-to-use interface . they build powerful keyphrase extraction models that achieve state-of-the-art results . |
| Outcome: | The proposed model achieves state-of-the-art on two public benchmarks and is easy to use and use. |
Scene Graph Modification Based on Natural Language Commands (2020.findings-emnlp)
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Xuanli He, Quan Hung Tran, Gholamreza Haffari, Walter Chang, Zhe Lin, Trung Bui, Franck Dernoncourt, Nhan Dam
| Challenge: | Numerous parsing methods have been developed for a single sentence, while a typical human-computer interaction session or conversation is not singleturn. |
| Approach: | They propose to modify an existing scene graph given a new user's command by using graph-based sparse transformer and cross attention information fusion to improve performance. |
| Outcome: | The proposed models outperform previous systems adapted from the machine translation and graph generation literature and contribute to the research community. |
Fine-grained Image Captioning with CLIP Reward (2022.findings-naacl)
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| Challenge: | Modern image captioning models are usually trained with text similarity objectives . reference captions often describe only the most salient objects in images . |
| Approach: | They propose to use CLIP to calculate multi-modal similarity and use it as a reward function . they propose a simple finetuning strategy to improve grammar that does not require extra text annotation. |
| Outcome: | The proposed model generates more distinctive captions than the CIDEroptimized model on text-to-image retrieval and fineCapEval. |
Fine-tuning CLIP Text Encoders with Two-step Paraphrasing (2024.findings-eacl)
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| Challenge: | Contrastive language-image pre-training models have demonstrated considerable success across various vision-language tasks, such as text-to-image retrieval. |
| Approach: | They propose a fine-tuning approach to enhance the representations of CLIP models for paraphrases by leveraging large language models. |
| Outcome: | The proposed model improves on baseline models across paraphrased retrieval, visual genome relation and attribution, and seven semantic textual similarity tasks. |
A Context-Dependent Gated Module for Incorporating Symbolic Semantics into Event Coreference Resolution (2021.naacl-main)
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| Challenge: | Existing methods for event coreference resolution use symbolic features, but they are noisy and contain errors. |
| Approach: | They propose a context-dependent gated module to adaptively control the information flows from the input symbolic features. |
| Outcome: | The proposed model achieves state-of-the-art on two datasets: ACE 2005 and KBP 2016 . |
Instruction Tuning with and without Context: Behavioral Shifts and Downstream Impact (2026.eacl-long)
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Hyunji Lee, Seunghyun Yoon, Yunjae Won, Hanseok Oh, Geewook Kim, Trung Bui, Franck Dernoncourt, Elias Stengel-Eskin, Mohit Bansal, Minjoon Seo
| Challenge: | Prior work on instruction tuning datasets combined these data types without examining their distinct effects. |
| Approach: | They investigate how training LLMs with or without context affects model behavior and performance . they find that using context-augmented data as the backbone for vision-language models reduces hallucination . |
| Outcome: | The proposed training with context-augmented data reduces hallucination and improves grounding in the visual domain. |
GUI Agents: A Survey (2025.findings-acl)
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Dang Nguyen, Jian Chen, Yu Wang, Gang Wu, Namyong Park, Zhengmian Hu, Hanjia Lyu, Junda Wu, Ryan Aponte, Yu Xia, Xintong Li, Jing Shi, Hongjie Chen, Viet Dac Lai, Zhouhang Xie, Sungchul Kim, Ruiyi Zhang, Tong Yu, Mehrab Tanjim, Nesreen K. Ahmed, Puneet Mathur, Seunghyun Yoon, Lina Yao, Branislav Kveton, Jihyung Kil, Thien Huu Nguyen, Trung Bui, Tianyi Zhou, Ryan A. Rossi, Franck Dernoncourt
| Challenge: | Large Foundation Models (LFMs) have transformed the landscape of AI research and day-to-day life. |
| Approach: | They propose a framework that delineates GUI agents' perception, reasoning, planning, and acting capabilities. |
| Outcome: | The proposed framework delineates their perception, reasoning, planning, and acting capabilities. |
History for Visual Dialog: Do we really need it? (2020.acl-main)
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| Challenge: | Recent studies have shown that dialog-based interaction grounded in visual information is not as effective as previous VQA tasks because of its dialog history. |
| Approach: | They propose a visual dialogue subset which explicitly encodes dialog history and a NDCG benchmark of 63%. |
| Outcome: | The proposed subset (VisdialConv) of the VisdialVal set achieves state-of-the-art performance on 72 % of the data. |
SlimLM: An Efficient Small Language Model for On-Device Document Assistance (2025.acl-demo)
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| Challenge: | Small language models (SLMs) show promise for mobile deployment, but their real world performance and applications on smartphones remain understudied. |
| Approach: | They propose a slim language model with a model size of 125M to 8B and a context length of 8B for efficient on-device processing. |
| Outcome: | The proposed model is based on a Samsung Galaxy S24 and shows comparable or superior performance. |
Edit me: A Corpus and a Framework for Understanding Natural Language Image Editing (L18-1)
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Ramesh Manuvinakurike, Jacqueline Brixey, Trung Bui, Walter Chang, Doo Soon Kim, Ron Artstein, Kallirroi Georgila
| Challenge: | a corpus of image edit requests is elicited for real world images, and an annotation framework is developed . evaluators evaluate crowd-sourced annotation as a means of efficiently creating a sizable corpus at a reasonable cost. |
| Approach: | They propose a natural language interface for interacting with an image editing program . they propose an annotation framework for understanding natural language requests . |
| Outcome: | The proposed tool interprets image edit requests and maps them to actionable commands. |
A Gated Self-attention Memory Network for Answer Selection (D19-1)
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| Challenge: | Existing deep learning approaches for answer selection use word-level comparison followed by aggregation. |
| Approach: | They propose a new gated self-attention memory network for answer selection task . they combine a transfer learning technique from a large-scale online corpus to create a gated network . |
| Outcome: | The proposed model outperforms existing methods on two standard answer selection datasets: TrecQA and WikiQA. |
A Gradually Soft Multi-Task and Data-Augmented Approach to Medical Question Understanding (2021.acl-long)
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Khalil Mrini, Franck Dernoncourt, Seunghyun Yoon, Trung Bui, Walter Chang, Emilia Farcas, Ndapa Nakashole
| Challenge: | Existing methods for medical question understanding often fail to provide high recall in answer retrieval. |
| Approach: | They propose a multi-task learning method with data augmentation for medical question understanding that uses just one dataset to optimize for both tasks. |
| Outcome: | The proposed method outperforms existing MTL methods across 4 datasets of medical question pairs in ROUGE scores, RQE accuracy and human evaluation. |
Variational Hierarchical Dialog Autoencoder for Dialog State Tracking Data Augmentation (2020.emnlp-main)
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| Challenge: | Recent studies have shown that generative data augmentation, where synthetic samples generated from deep generative models complement the training dataset, benefit NLP tasks. |
| Approach: | They propose a Variational Hierarchical Dialog Autoencoder for modeling the complete aspects of goal-oriented dialogs using inter-connected latent variables and learns to generate coherent dialogs from the latent spaces. |
| Outcome: | The proposed model outperforms previous strong baselines on dialog response generation and user simulation tasks. |
Offensive Content Detection via Synthetic Code-Switched Text (2022.coling-1)
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| Challenge: | Existing methods to detect offensive content in social media platforms are limited by the availability of labeled code-switched data. |
| Approach: | They propose a method for generating synthetic code-switched offensive content data using human-generated data and a keyword classification baseline. |
| Outcome: | The proposed algorithm can be used to generate synthetic code-switched offensive content data and train it on human-generated data. |
PhotoshopQuiA: A Corpus of Non-Factoid Questions and Answers for Why-Question Answering (L18-1)
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| Challenge: | Community Question Answering web sites are used for non-factoid question answering . however, there is a scarcity of available datasets for this task . cnn.com's john m. sutter is releasing a dataset for why-QA . |
| Approach: | They propose a dataset of 2,854 why-question and answer(s) pairs related to Adobe Photoshop usage from five CQA web sites. |
| Outcome: | The new dataset is the first English dataset for Why-QA that focuses on a product . it can be used to build Why-Q systems, evaluate approaches and develop new models . |
X-METRA-ADA: Cross-lingual Meta-Transfer learning Adaptation to Natural Language Understanding and Question Answering (2021.naacl-main)
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| Challenge: | Multilingual models have gained popularity for their zero-shot cross-lingual transfer learning capabilities, but their generalization ability is inconsistent for typologically diverse languages. |
| Approach: | They propose a meta-learning approach that adapts MAML to learn to adapt to new languages . they extensively evaluate two cross-lingual NLU tasks using English as source and spanish as target . |
| Outcome: | The proposed approach outperforms naive fine-tuning on cross-lingual tasks for most languages. |