paper_id string | title string | paper_url string | pdf_url string | authors list | abstract large_string | track string | primary_area string | doi string | volume string | issue string | pages string | abstract_source string | arxiv_id string | arxiv_id_source string |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
10.1609/aaai.v35i14.17457 | Escaping Local Optima with Non-Elitist Evolutionary Algorithms | https://ojs.aaai.org/index.php/AAAI/article/view/17457 | https://ojs.aaai.org/index.php/AAAI/article/download/17457/17264 | [
"Duc-Cuong Dang",
"Anton Eremeev",
"Per Kristian Lehre"
] | Most discrete evolutionary algorithms (EAs) implement elitism, meaning that they make the biologically implausible assumption that the fittest individuals never die. While elitism favours exploitation and ensures that the best seen solutions are not lost, it has been widely conjectured that non-elitism is necessary to ... | main | Search and Optimization | 10.1609/aaai.v35i14.17457 | 35 | 14 | 12275-12283 | official | null | null |
10.1609/aaai.v35i14.17456 | Symmetry Breaking for k-Robust Multi-Agent Path Finding | https://ojs.aaai.org/index.php/AAAI/article/view/17456 | https://ojs.aaai.org/index.php/AAAI/article/download/17456/17263 | [
"Zhe Chen",
"Daniel D. Harabor",
"Jiaoyang Li",
"Peter J. Stuckey"
] | During Multi-Agent Path Finding (MAPF) problems, agentscan be delayed by unexpected events. To address suchsituations recent work describes k-Robust Conflict-BasedSearch (k-CBS): an algorithm that produces coordinated andcollision-free plan that is robust for up tokdelays. In thiswork we introducing a variety of pairwi... | main | Search and Optimization | 10.1609/aaai.v35i14.17456 | 35 | 14 | 12267-12274 | official | 2102.08689 | title_snapshot |
10.1609/aaai.v35i14.17455 | NuQClq: An Effective Local Search Algorithm for Maximum Quasi-Clique Problem | https://ojs.aaai.org/index.php/AAAI/article/view/17455 | https://ojs.aaai.org/index.php/AAAI/article/download/17455/17262 | [
"Jiejiang Chen",
"Shaowei Cai",
"Shiwei Pan",
"Yiyuan Wang",
"Qingwei Lin",
"Mengyu Zhao",
"Minghao Yin"
] | The maximum quasi-clique problem (MQCP) is an important extension of maximum clique problem with wide applications. Recent heuristic MQCP algorithms can hardly solve large and hard graphs effectively. This paper develops an efficient local search algorithm named NuQClq for the MQCP, which has two main ideas. First, we ... | main | Search and Optimization | 10.1609/aaai.v35i14.17455 | 35 | 14 | 12258-12266 | official | null | null |
10.1609/aaai.v35i14.17454 | Parameterized Algorithms for MILPs with Small Treedepth | https://ojs.aaai.org/index.php/AAAI/article/view/17454 | https://ojs.aaai.org/index.php/AAAI/article/download/17454/17261 | [
"Cornelius Brand",
"Martin Koutecký",
"Sebastian Ordyniak"
] | Solving (mixed) integer (linear) programs, (M)I(L)Ps for short, is a fundamental optimisation task with a wide range of applications in artificial intelligence and computer science in general. While hard in general, recent years have brought about vast progress for solving structurally restricted, (non-mixed) ILPs: n-f... | main | Search and Optimization | 10.1609/aaai.v35i14.17454 | 35 | 14 | 12249-12257 | official | 1912.03501 | title_snapshot |
10.1609/aaai.v35i14.17453 | f-Aware Conflict Prioritization & Improved Heuristics For Conflict-Based Search | https://ojs.aaai.org/index.php/AAAI/article/view/17453 | https://ojs.aaai.org/index.php/AAAI/article/download/17453/17260 | [
"Eli Boyarski",
"Ariel Felner",
"Pierre Le Bodic",
"Daniel D. Harabor",
"Peter J. Stuckey",
"Sven Koenig"
] | Conflict-Based Search (CBS) is a leading two-level algorithm for optimal Multi-Agent Path Finding (MAPF). The main step of CBS is to expand nodes by resolving conflicts (where two agents collide). Choosing the ‘right’ conflict to resolve can greatly speed up the search. CBS first resolves conflicts where the costs (g-v... | main | Search and Optimization | 10.1609/aaai.v35i14.17453 | 35 | 14 | 12241-12248 | official | null | null |
10.1609/aaai.v35i14.17452 | Combining Preference Elicitation with Local Search and Greedy Search for Matroid Optimization | https://ojs.aaai.org/index.php/AAAI/article/view/17452 | https://ojs.aaai.org/index.php/AAAI/article/download/17452/17259 | [
"Nawal Benabbou",
"Cassandre Leroy",
"Thibaut Lust",
"Patrice Perny"
] | We propose two incremental preference elicitation methods for interactive preference-based optimization on weighted matroid structures. More precisely, for linear objective (utility) functions, we propose an interactive greedy algorithm interleaving preference queries with the incremental construction of an independent... | main | Search and Optimization | 10.1609/aaai.v35i14.17452 | 35 | 14 | 12233-12240 | official | null | null |
10.1609/aaai.v35i14.17451 | Generalization in Portfolio-Based Algorithm Selection | https://ojs.aaai.org/index.php/AAAI/article/view/17451 | https://ojs.aaai.org/index.php/AAAI/article/download/17451/17258 | [
"Maria-Florina Balcan",
"Tuomas Sandholm",
"Ellen Vitercik"
] | Portfolio-based algorithm selection has seen tremendous practical success over the past two decades. This algorithm configuration procedure works by first selecting a portfolio of diverse algorithm parameter settings, and then, on a given problem instance, using an algorithm selector to choose a parameter setting from ... | main | Search and Optimization | 10.1609/aaai.v35i14.17451 | 35 | 14 | 12225-12232 | official | 2012.13315 | title_snapshot |
10.1609/aaai.v35i14.17559 | Multi-SpectroGAN: High-Diversity and High-Fidelity Spectrogram Generation with Adversarial Style Combination for Speech Synthesis | https://ojs.aaai.org/index.php/AAAI/article/view/17559 | https://ojs.aaai.org/index.php/AAAI/article/download/17559/17366 | [
"Sang-Hoon Lee",
"Hyun-Wook Yoon",
"Hyeong-Rae Noh",
"Ji-Hoon Kim",
"Seong-Whan Lee"
] | While generative adversarial networks (GANs) based neural text-to-speech (TTS) systems have shown significant improvement in neural speech synthesis, there is no TTS system to learn to synthesize speech from text sequences with only adversarial feedback. Because adversarial feedback alone is not sufficient to train the... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i14.17559 | 35 | 14 | 13198-13206 | official | 2012.07267 | title_snapshot |
10.1609/aaai.v35i14.17543 | SARG: A Novel Semi Autoregressive Generator for Multi-turn Incomplete Utterance Restoration | https://ojs.aaai.org/index.php/AAAI/article/view/17543 | https://ojs.aaai.org/index.php/AAAI/article/download/17543/17350 | [
"Mengzuo Huang",
"Feng Li",
"Wuhe Zou",
"Weidong Zhang"
] | Dialogue systems in open domain have achieved great success due to the easily obtained single-turn corpus and the development of deep learning, but the multi-turn scenario is still a challenge because of the frequent coreference and information omission. In this paper, we investigate the incomplete utterance restoratio... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i14.17543 | 35 | 14 | 13055-13063 | official | 2008.01474 | title_snapshot |
10.1609/aaai.v35i14.17544 | Entity Guided Question Generation with Contextual Structure and Sequence Information Capturing | https://ojs.aaai.org/index.php/AAAI/article/view/17544 | https://ojs.aaai.org/index.php/AAAI/article/download/17544/17351 | [
"Qingbao Huang",
"Mingyi Fu",
"Linzhang Mo",
"Yi Cai",
"Jingyun Xu",
"Pijian Li",
"Qing Li",
"Ho-fung Leung"
] | Question generation is a challenging task and has attracted widespread attention in recent years. Although previous studies have made great progress, there are still two main shortcomings: First, previous work did not simultaneously capture the sequence information and structure information hidden in the context, which... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i14.17544 | 35 | 14 | 13064-13072 | official | null | null |
10.1609/aaai.v35i14.17545 | Story Ending Generation with Multi-Level Graph Convolutional Networks over Dependency Trees | https://ojs.aaai.org/index.php/AAAI/article/view/17545 | https://ojs.aaai.org/index.php/AAAI/article/download/17545/17352 | [
"Qingbao Huang",
"Linzhang Mo",
"Pijian Li",
"Yi Cai",
"Qingguang Liu",
"Jielong Wei",
"Qing Li",
"Ho-fung Leung"
] | As an interesting and challenging task, story ending generation aims at generating a reasonable and coherent ending for a given story context. The key challenge of the task is to comprehend the context sufficiently and capture the hidden logic information effectively, which has not been well explored by most existing g... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i14.17545 | 35 | 14 | 13073-13081 | official | null | null |
10.1609/aaai.v35i14.17546 | Adaptive Beam Search Decoding for Discrete Keyphrase Generation | https://ojs.aaai.org/index.php/AAAI/article/view/17546 | https://ojs.aaai.org/index.php/AAAI/article/download/17546/17353 | [
"Xiaoli Huang",
"Tongge Xu",
"Lvan Jiao",
"Yueran Zu",
"Youmin Zhang"
] | Keyphrase Generation compresses a document into some highly-summative phrases, which is an important task in natural language processing. Most state-of-the-art adopt greedy search or beam search decoding methods. These two decoding methods generate a large number of duplicated keyphrases and are time-consuming. Moreove... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i14.17546 | 35 | 14 | 13082-13089 | official | null | null |
10.1609/aaai.v35i14.17547 | Distribution Matching for Rationalization | https://ojs.aaai.org/index.php/AAAI/article/view/17547 | https://ojs.aaai.org/index.php/AAAI/article/download/17547/17354 | [
"Yongfeng Huang",
"Yujun Chen",
"Yulun Du",
"Zhilin Yang"
] | The task of rationalization aims to extract pieces of input text as rationales to justify neural network predictions on text classification tasks. By definition, rationales represent key text pieces used for prediction and thus should have similar classification feature distribution compared to the original input text.... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i14.17547 | 35 | 14 | 13090-13097 | official | 2106.00320 | title_snapshot |
10.1609/aaai.v35i14.17548 | Audio-Oriented Multimodal Machine Comprehension via Dynamic Inter- and Intra-modality Attention | https://ojs.aaai.org/index.php/AAAI/article/view/17548 | https://ojs.aaai.org/index.php/AAAI/article/download/17548/17355 | [
"Zhiqi Huang",
"Fenglin Liu",
"Xian Wu",
"Shen Ge",
"Helin Wang",
"Wei Fan",
"Yuexian Zou"
] | While Machine Comprehension (MC) has attracted extensive research interests in recent years, existing approaches mainly belong to the category of Machine Reading Comprehension task which mines textual inputs (paragraphs and questions) to predict the answers (choices or text spans). However, there are a lot of MC tasks ... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i14.17548 | 35 | 14 | 13098-13106 | official | 2107.01571 | title_judge |
10.1609/aaai.v35i14.17549 | Unsupervised Learning of Discourse Structures using a Tree Autoencoder | https://ojs.aaai.org/index.php/AAAI/article/view/17549 | https://ojs.aaai.org/index.php/AAAI/article/download/17549/17356 | [
"Patrick Huber",
"Giuseppe Carenini"
] | Discourse information, as postulated by popular discourse theories, such as RST and PDTB, has been shown to improve an increasing number of downstream NLP tasks, showing positive effects and synergies of discourse with important real-world applications. While methods for incorporating discourse become more and more sop... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i14.17549 | 35 | 14 | 13107-13115 | official | 2012.09446 | title_snapshot |
10.1609/aaai.v35i14.17550 | Dynamic Hybrid Relation Exploration Network for Cross-Domain Context-Dependent Semantic Parsing | https://ojs.aaai.org/index.php/AAAI/article/view/17550 | https://ojs.aaai.org/index.php/AAAI/article/download/17550/17357 | [
"Binyuan Hui",
"Ruiying Geng",
"Qiyu Ren",
"Binhua Li",
"Yongbin Li",
"Jian Sun",
"Fei Huang",
"Luo Si",
"Pengfei Zhu",
"Xiaodan Zhu"
] | Semantic parsing has long been a fundamental problem in natural language processing. Recently, cross-domain context-dependent semantic parsing has become a new focus of research. Central to the problem is the challenge of leveraging contextual information of both natural language queries and database schemas in the int... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i14.17550 | 35 | 14 | 13116-13124 | official | 2101.01686 | title_judge |
10.1609/aaai.v35i14.17552 | Flexible Non-Autoregressive Extractive Summarization with Threshold: How to Extract a Non-Fixed Number of Summary Sentences | https://ojs.aaai.org/index.php/AAAI/article/view/17552 | https://ojs.aaai.org/index.php/AAAI/article/download/17552/17359 | [
"Ruipeng Jia",
"Yanan Cao",
"Haichao Shi",
"Fang Fang",
"Pengfei Yin",
"Shi Wang"
] | Sentence-level extractive summarization is a fundamental yet challenging task, and recent powerful approaches prefer to pick sentences sorted by the predicted probabilities until the length limit is reached, a.k.a. ``Top-K Strategy''. This length limit is fixed based on the validation set, resulting in the lack of flex... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i14.17552 | 35 | 14 | 13134-13142 | official | null | null |
10.1609/aaai.v35i14.17553 | EQG-RACE: Examination-Type Question Generation | https://ojs.aaai.org/index.php/AAAI/article/view/17553 | https://ojs.aaai.org/index.php/AAAI/article/download/17553/17360 | [
"Xin Jia",
"Wenjie Zhou",
"Xu Sun",
"Yunfang Wu"
] | Question Generation (QG) is an essential component of the automatic intelligent tutoring systems, which aims to generate high-quality questions for facilitating the reading practice and assessments. However, existing QG technologies encounter several key issues concerning the biased and unnatural language sources of da... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i14.17553 | 35 | 14 | 13143-13151 | official | 2012.06106 | title_snapshot |
10.1609/aaai.v35i14.17554 | Hierarchical Macro Discourse Parsing Based on Topic Segmentation | https://ojs.aaai.org/index.php/AAAI/article/view/17554 | https://ojs.aaai.org/index.php/AAAI/article/download/17554/17361 | [
"Feng Jiang",
"Yaxin Fan",
"Xiaomin Chu",
"Peifeng Li",
"Qiaoming Zhu",
"Fang Kong"
] | Hierarchically constructing micro (i.e., intra-sentence or inter-sentence) discourse structure trees using explicit boundaries (e.g., sentence and paragraph boundaries) has been proved to be an effective strategy. However, it is difficult to apply this strategy to document-level macro (i.e., inter-paragraph) discourse ... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i14.17554 | 35 | 14 | 13152-13160 | official | null | null |
10.1609/aaai.v35i14.17555 | FIXMYPOSE: Pose Correctional Captioning and Retrieval | https://ojs.aaai.org/index.php/AAAI/article/view/17555 | https://ojs.aaai.org/index.php/AAAI/article/download/17555/17362 | [
"Hyounghun Kim",
"Abhay Zala",
"Graham Burri",
"Mohit Bansal"
] | Interest in physical therapy and individual exercises such as yoga/dance has increased alongside the well-being trend, and people globally enjoy such exercises at home/office via video streaming platforms. However, such exercises are hard to follow without expert guidance. Even if experts can help, it is almost impossi... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i14.17555 | 35 | 14 | 13161-13170 | official | 2104.01703 | title_snapshot |
10.1609/aaai.v35i14.17556 | Self-supervised Pre-training and Contrastive Representation Learning for Multiple-choice Video QA | https://ojs.aaai.org/index.php/AAAI/article/view/17556 | https://ojs.aaai.org/index.php/AAAI/article/download/17556/17363 | [
"Seonhoon Kim",
"Seohyeong Jeong",
"Eunbyul Kim",
"Inho Kang",
"Nojun Kwak"
] | Video Question Answering (VideoQA) requires fine-grained understanding of both video and language modalities to answer the given questions. In this paper, we propose novel training schemes for multiple-choice video question answering with a self-supervised pre-training stage and a supervised contrastive learning in the... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i14.17556 | 35 | 14 | 13171-13179 | official | 2009.08043 | title_snapshot |
10.1609/aaai.v35i14.17557 | The Gap on Gap: Tackling the Problem of Differing Data Distributions in Bias-Measuring Datasets | https://ojs.aaai.org/index.php/AAAI/article/view/17557 | https://ojs.aaai.org/index.php/AAAI/article/download/17557/17364 | [
"Vid Kocijan",
"Oana-Maria Camburu",
"Thomas Lukasiewicz"
] | Diagnostic datasets that can detect biased models are an important prerequisite for bias reduction within natural language processing. However, undesired patterns in the collected data can make such tests incorrect. For example, if the feminine subset of a gender-bias-measuring coreference resolution dataset contains s... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i14.17557 | 35 | 14 | 13180-13188 | official | 2011.01837 | title_snapshot |
10.1609/aaai.v35i14.17558 | SALNet: Semi-supervised Few-Shot Text Classification with Attention-based Lexicon Construction | https://ojs.aaai.org/index.php/AAAI/article/view/17558 | https://ojs.aaai.org/index.php/AAAI/article/download/17558/17365 | [
"Ju-Hyoung Lee",
"Sang-Ki Ko",
"Yo-Sub Han"
] | We propose a semi-supervised bootstrap learning framework for few-shot text classification. From a small amount of the initial dataset, our framework obtains a larger set of reliable training data by using the attention weights from an LSTM-based trained classifier. We first train an LSTM-based text classifier from a g... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i14.17558 | 35 | 14 | 13189-13197 | official | null | null |
10.1609/aaai.v35i14.17551 | DDRel: A New Dataset for Interpersonal Relation Classification in Dyadic Dialogues | https://ojs.aaai.org/index.php/AAAI/article/view/17551 | https://ojs.aaai.org/index.php/AAAI/article/download/17551/17358 | [
"Qi Jia",
"Hongru Huang",
"Kenny Q. Zhu"
] | Interpersonal language style shifting in dialogues is an interesting and almost instinctive ability of human. Understanding interpersonal relationship from language content is also a crucial step toward further understanding dialogues. Previous work mainly focuses on relation extraction between named entities in texts ... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i14.17551 | 35 | 14 | 13125-13133 | official | 2012.02553 | title_snapshot |
10.1609/aaai.v35i14.17532 | Sketch and Customize: A Counterfactual Story Generator | https://ojs.aaai.org/index.php/AAAI/article/view/17532 | https://ojs.aaai.org/index.php/AAAI/article/download/17532/17339 | [
"Changying Hao",
"Liang Pang",
"Yanyan Lan",
"Yan Wang",
"Jiafeng Guo",
"Xueqi Cheng"
] | Recent text generation models are easy to generate relevant and fluent text for the given text, while lack of causal reasoning ability when we change some parts of the given text. Counterfactual story rewriting is a recently proposed task to test the causal reasoning ability for text generation models, which requires a... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i14.17532 | 35 | 14 | 12955-12962 | official | 2104.00929 | title_snapshot |
10.1609/aaai.v35i14.17531 | BERT & Family Eat Word Salad: Experiments with Text Understanding | https://ojs.aaai.org/index.php/AAAI/article/view/17531 | https://ojs.aaai.org/index.php/AAAI/article/download/17531/17338 | [
"Ashim Gupta",
"Giorgi Kvernadze",
"Vivek Srikumar"
] | In this paper, we study the response of large models from the BERT family to incoherent inputs that should confuse any model that claims to understand natural language. We define simple heuristics to construct such examples. Our experiments show that state-of-the-art models consistently fail to recognize them as ill-fo... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i14.17531 | 35 | 14 | 12946-12954 | official | 2101.03453 | title_snapshot |
10.1609/aaai.v35i14.17530 | Iterative Utterance Segmentation for Neural Semantic Parsing | https://ojs.aaai.org/index.php/AAAI/article/view/17530 | https://ojs.aaai.org/index.php/AAAI/article/download/17530/17337 | [
"Yinuo Guo",
"Zeqi Lin",
"Jian-Guang Lou",
"Dongmei Zhang"
] | Neural semantic parsers usually fail to parse long and complex utterances into correct meaning representations, due to the lack of exploiting the principle of compositionality. To address this issue, we present a novel framework for boosting neural semantic parsers via iterative utterance segmentation. Given an input u... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i14.17530 | 35 | 14 | 12937-12945 | official | 2012.07019 | title_snapshot |
10.1609/aaai.v35i14.17529 | Label Confusion Learning to Enhance Text Classification Models | https://ojs.aaai.org/index.php/AAAI/article/view/17529 | https://ojs.aaai.org/index.php/AAAI/article/download/17529/17336 | [
"Biyang Guo",
"Songqiao Han",
"Xiao Han",
"Hailiang Huang",
"Ting Lu"
] | Representing the true label as one-hot vector is the common practice in training text classification models. However, the one-hot representation may not adequately reflect the relation between the instance and labels, as labels are often not completely independent and instances may relate to multiple labels in practice... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i14.17529 | 35 | 14 | 12929-12936 | official | 2012.04987 | title_snapshot |
10.1609/aaai.v35i14.17528 | Read, Retrospect, Select: An MRC Framework to Short Text Entity Linking | https://ojs.aaai.org/index.php/AAAI/article/view/17528 | https://ojs.aaai.org/index.php/AAAI/article/download/17528/17335 | [
"Yingjie Gu",
"Xiaoye Qu",
"Zhefeng Wang",
"Baoxing Huai",
"Nicholas Jing Yuan",
"Xiaolin Gui"
] | Entity linking (EL) for the rapidly growing short text (e.g. search queries and news titles) is critical to industrial applications. Most existing approaches relying on adequate context for long text EL are not effective for the concise and sparse short text. In this paper, we propose a novel framework called Multi-tur... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i14.17528 | 35 | 14 | 12920-12928 | official | 2101.02394 | title_snapshot |
10.1609/aaai.v35i14.17527 | DialogBERT: Discourse-Aware Response Generation via Learning to Recover and Rank Utterances | https://ojs.aaai.org/index.php/AAAI/article/view/17527 | https://ojs.aaai.org/index.php/AAAI/article/download/17527/17334 | [
"Xiaodong Gu",
"Kang Min Yoo",
"Jung-Woo Ha"
] | Recent advances in pre-trained language models have significantly improved neural response generation. However, existing methods usually view the dialogue context as a linear sequence of tokens and learn to generate the next word through token-level self-attention. Such token-level encoding hinders the exploration of d... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i14.17527 | 35 | 14 | 12911-12919 | official | 2012.01775 | title_snapshot |
10.1609/aaai.v35i14.17526 | Perception Score: A Learned Metric for Open-ended Text Generation Evaluation | https://ojs.aaai.org/index.php/AAAI/article/view/17526 | https://ojs.aaai.org/index.php/AAAI/article/download/17526/17333 | [
"Jing Gu",
"Qingyang Wu",
"Zhou Yu"
] | Automatic evaluation for open-ended natural language generation tasks remains a challenge. We propose a learned evaluation metric: Perception Score. It utilizes a pre-trained model and considers context information for conditional generation. Perception Score assigns a holistic score along with the uncertainty measurem... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i14.17526 | 35 | 14 | 12902-12910 | official | 2008.03082 | title_snapshot |
10.1609/aaai.v35i14.17525 | Fake it Till You Make it: Self-Supervised Semantic Shifts for Monolingual Word Embedding Tasks | https://ojs.aaai.org/index.php/AAAI/article/view/17525 | https://ojs.aaai.org/index.php/AAAI/article/download/17525/17332 | [
"Maurício Gruppi",
"Pin-Yu Chen",
"Sibel Adali"
] | The use of language is subject to variation over time as well as across social groups and knowledge domains, leading to differences even in the monolingual scenario. Such variation in word usage is often called lexical semantic change (LSC). The goal of LSC is to characterize and quantify language variations with respe... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i14.17525 | 35 | 14 | 12893-12901 | official | 2102.00290 | title_snapshot |
10.1609/aaai.v35i14.17524 | Analogy Training Multilingual Encoders | https://ojs.aaai.org/index.php/AAAI/article/view/17524 | https://ojs.aaai.org/index.php/AAAI/article/download/17524/17331 | [
"Nicolas Garneau",
"Mareike Hartmann",
"Anders Sandholm",
"Sebastian Ruder",
"Ivan Vulić",
"Anders Søgaard"
] | Language encoders encode words and phrases in ways that capture their local semantic relatedness, but are known to be globally inconsistent. Global inconsistency can seemingly be corrected for, in part, by leveraging signals from knowledge bases, but previous results are partial and limited to monolingual English encod... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i14.17524 | 35 | 14 | 12884-12892 | official | null | null |
10.1609/aaai.v35i14.17523 | Question-Driven Span Labeling Model for Aspect–Opinion Pair Extraction | https://ojs.aaai.org/index.php/AAAI/article/view/17523 | https://ojs.aaai.org/index.php/AAAI/article/download/17523/17330 | [
"Lei Gao",
"Yulong Wang",
"Tongcun Liu",
"Jingyu Wang",
"Lei Zhang",
"Jianxin Liao"
] | Aspect term extraction and opinion word extraction are two fundamental subtasks of aspect-based sentiment analysis. The internal relationship between aspect terms and opinion words is typically ignored, and information for the decision-making of buyers and sellers is insufficient. In this paper, we explore an aspect–op... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i14.17523 | 35 | 14 | 12875-12883 | official | null | null |
10.1609/aaai.v35i14.17540 | C2C-GenDA: Cluster-to-Cluster Generation for Data Augmentation of Slot Filling | https://ojs.aaai.org/index.php/AAAI/article/view/17540 | https://ojs.aaai.org/index.php/AAAI/article/download/17540/17347 | [
"Yutai Hou",
"Sanyuan Chen",
"Wanxiang Che",
"Cheng Chen",
"Ting Liu"
] | Slot filling, a fundamental module of spoken language understanding, often suffers from insufficient quantity and diversity of training data. To remedy this, we propose a novel Cluster-to-Cluster generation framework for Data Augmentation (DA), named C2C-GenDA. It enlarges the training set by reconstructing existing ut... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i14.17540 | 35 | 14 | 13027-13035 | official | 2012.07004 | title_snapshot |
10.1609/aaai.v35i14.17533 | Self-Attention Attribution: Interpreting Information Interactions Inside Transformer | https://ojs.aaai.org/index.php/AAAI/article/view/17533 | https://ojs.aaai.org/index.php/AAAI/article/download/17533/17340 | [
"Yaru Hao",
"Li Dong",
"Furu Wei",
"Ke Xu"
] | The great success of Transformer-based models benefits from the powerful multi-head self-attention mechanism, which learns token dependencies and encodes contextual information from the input. Prior work strives to attribute model decisions to individual input features with different saliency measures, but they fail to... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i14.17533 | 35 | 14 | 12963-12971 | official | 2004.11207 | title_snapshot |
10.1609/aaai.v35i14.17534 | Humor Knowledge Enriched Transformer for Understanding Multimodal Humor | https://ojs.aaai.org/index.php/AAAI/article/view/17534 | https://ojs.aaai.org/index.php/AAAI/article/download/17534/17341 | [
"Md Kamrul Hasan",
"Sangwu Lee",
"Wasifur Rahman",
"Amir Zadeh",
"Rada Mihalcea",
"Louis-Philippe Morency",
"Ehsan Hoque"
] | Recognizing humor from a video utterance requires understanding the verbal and non-verbal components as well as incorporating the appropriate context and external knowledge. In this paper, we propose Humor Knowledge enriched Transformer (HKT) that can capture the gist of a multimodal humorous expression by integrating ... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i14.17534 | 35 | 14 | 12972-12980 | official | null | null |
10.1609/aaai.v35i14.17535 | Synchronous Interactive Decoding for Multilingual Neural Machine Translation | https://ojs.aaai.org/index.php/AAAI/article/view/17535 | https://ojs.aaai.org/index.php/AAAI/article/download/17535/17342 | [
"Hao He",
"Qian Wang",
"Zhipeng Yu",
"Yang Zhao",
"Jiajun Zhang",
"Chengqing Zong"
] | To simultaneously translate a source language into multiple different target languages is one of the most common scenarios of multilingual translation. However, existing methods cannot make full use of translation model information during decoding, such as intra-lingual and inter-lingual future information, and therefo... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i14.17535 | 35 | 14 | 12981-12988 | official | null | null |
10.1609/aaai.v35i14.17536 | Show Me How To Revise: Improving Lexically Constrained Sentence Generation with XLNet | https://ojs.aaai.org/index.php/AAAI/article/view/17536 | https://ojs.aaai.org/index.php/AAAI/article/download/17536/17343 | [
"Xingwei He",
"Victor O.K. Li"
] | Lexically constrained sentence generation allows the incorporation of prior knowledge such as lexical constraints into the output. This technique has been applied to machine translation, and dialog response generation. Previous work usually used Markov Chain Monte Carlo (MCMC) sampling to generate lexically constrained... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i14.17536 | 35 | 14 | 12989-12997 | official | 2109.05797 | title_snapshot |
10.1609/aaai.v35i14.17537 | Towards Fully Automated Manga Translation | https://ojs.aaai.org/index.php/AAAI/article/view/17537 | https://ojs.aaai.org/index.php/AAAI/article/download/17537/17344 | [
"Ryota Hinami",
"Shonosuke Ishiwatari",
"Kazuhiko Yasuda",
"Yusuke Matsui"
] | We tackle the problem of machine translation of manga, Japanese comics. Manga translation involves two important problems in machine translation: context-aware and multimodal translation. Since text and images are mixed up in an unstructured fashion in Manga, obtaining context from the image is essential for manga tran... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i14.17537 | 35 | 14 | 12998-13008 | official | 2012.14271 | title_snapshot |
10.1609/aaai.v35i14.17538 | SMART: A Situation Model for Algebra Story Problems via Attributed Grammar | https://ojs.aaai.org/index.php/AAAI/article/view/17538 | https://ojs.aaai.org/index.php/AAAI/article/download/17538/17345 | [
"Yining Hong",
"Qing Li",
"Ran Gong",
"Daniel Ciao",
"Siyuan Huang",
"Song-Chun Zhu"
] | Solving algebra story problems remains a challenging task in artificial intelligence, which requires a detailed understanding of real-world situations and a strong mathematical reasoning capability. Previous neural solvers of math word problems directly translate problem texts into equations, lacking an explicit interp... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i14.17538 | 35 | 14 | 13009-13017 | official | 2012.14011 | title_snapshot |
10.1609/aaai.v35i14.17539 | It Takes Two to Empathize: One to Seek and One to Provide | https://ojs.aaai.org/index.php/AAAI/article/view/17539 | https://ojs.aaai.org/index.php/AAAI/article/download/17539/17346 | [
"Mahshid Hosseini",
"Cornelia Caragea"
] | Empathy describes the capacity to feel, understand, and emotionally engage with what other people are experiencing. People have recently started to turn to online health communities to seek empathetic support when they undergo difficult situations such as suffering from a life-threatening disease, while others are ther... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i14.17539 | 35 | 14 | 13018-13026 | official | null | null |
10.1609/aaai.v35i14.17541 | Few-shot Learning for Multi-label Intent Detection | https://ojs.aaai.org/index.php/AAAI/article/view/17541 | https://ojs.aaai.org/index.php/AAAI/article/download/17541/17348 | [
"Yutai Hou",
"Yongkui Lai",
"Yushan Wu",
"Wanxiang Che",
"Ting Liu"
] | In this paper, we study the few-shot multi-label classification for user intent detection. For multi-label intent detection, state-of-the-art work estimates label-instance relevance scores and uses a threshold to select multiple associated intent labels. To determine appropriate thresholds with only a few examples, we ... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i14.17541 | 35 | 14 | 13036-13044 | official | 2010.05256 | title_snapshot |
10.1609/aaai.v35i14.17542 | HARGAN: Heterogeneous Argument Attention Network for Persuasiveness Prediction | https://ojs.aaai.org/index.php/AAAI/article/view/17542 | https://ojs.aaai.org/index.php/AAAI/article/download/17542/17349 | [
"Kuo-Yu Huang",
"Hen-Hsen Huang",
"Hsin-Hsi Chen"
] | Argument structure elaborates the relation among claims and premises. Previous works in persuasiveness prediction do not consider this relation in their architectures. To take argument structure information into account, this paper proposes an approach to persuasiveness prediction with a novel graph-based neural networ... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i14.17542 | 35 | 14 | 13045-13054 | official | null | null |
10.1609/aaai.v35i14.17519 | Nested Named Entity Recognition with Partially-Observed TreeCRFs | https://ojs.aaai.org/index.php/AAAI/article/view/17519 | https://ojs.aaai.org/index.php/AAAI/article/download/17519/17326 | [
"Yao Fu",
"Chuanqi Tan",
"Mosha Chen",
"Songfang Huang",
"Fei Huang"
] | Named entity recognition (NER) is a well-studied task in natural language processing. However, the widely-used sequence labeling framework is difficult to detect entities with nested structures. In this work, we view nested NER as constituency parsing with partially-observed trees and model it with partially-observed T... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i14.17519 | 35 | 14 | 12839-12847 | official | 2012.08478 | title_snapshot |
10.1609/aaai.v35i14.17522 | Judgment Prediction via Injecting Legal Knowledge into Neural Networks | https://ojs.aaai.org/index.php/AAAI/article/view/17522 | https://ojs.aaai.org/index.php/AAAI/article/download/17522/17329 | [
"Leilei Gan",
"Kun Kuang",
"Yi Yang",
"Fei Wu"
] | Legal Judgment Prediction (LJP) is a key problem in legal artificial intelligence, which is aimed to predict a law case's judgment based on a given text describing the facts of the law case. Most of the previous work treats LJP as a text classification task and generally adopts deep neural networks (DNNs) based methods... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i14.17522 | 35 | 14 | 12866-12874 | official | null | null |
10.1609/aaai.v35i14.17521 | Paragraph-level Commonsense Transformers with Recurrent Memory | https://ojs.aaai.org/index.php/AAAI/article/view/17521 | https://ojs.aaai.org/index.php/AAAI/article/download/17521/17328 | [
"Saadia Gabriel",
"Chandra Bhagavatula",
"Vered Shwartz",
"Ronan Le Bras",
"Maxwell Forbes",
"Yejin Choi"
] | Human understanding of narrative texts requires making commonsense inferences beyond what is stated in the text explicitly. A recent model, COMET, can generate such inferences along several dimensions such as pre- and post-conditions, motivations, and mental states of the participants. However, COMET was trained on sho... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i14.17521 | 35 | 14 | 12857-12865 | official | 2010.01486 | title_snapshot |
10.1609/aaai.v35i14.17520 | A Theoretical Analysis of the Repetition Problem in Text Generation | https://ojs.aaai.org/index.php/AAAI/article/view/17520 | https://ojs.aaai.org/index.php/AAAI/article/download/17520/17327 | [
"Zihao Fu",
"Wai Lam",
"Anthony Man-Cho So",
"Bei Shi"
] | Text generation tasks, including translation, summarization, language models, and etc. see rapid growth during recent years. Despite the remarkable achievements, the repetition problem has been observed in nearly all text generation models undermining the generation performance extensively. To solve the repetition prob... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i14.17520 | 35 | 14 | 12848-12856 | official | 2012.14660 | title_snapshot |
10.1609/aaai.v35i14.17518 | LRC-BERT: Latent-representation Contrastive Knowledge Distillation for Natural Language Understanding | https://ojs.aaai.org/index.php/AAAI/article/view/17518 | https://ojs.aaai.org/index.php/AAAI/article/download/17518/17325 | [
"Hao Fu",
"Shaojun Zhou",
"Qihong Yang",
"Junjie Tang",
"Guiquan Liu",
"Kaikui Liu",
"Xiaolong Li"
] | The pre-training models such as BERT have achieved great results in various natural language processing problems. However, a large number of parameters need significant amounts of memory and the consumption of inference time, which makes it difficult to deploy them on edge devices. In this work, we propose a knowledge ... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i14.17518 | 35 | 14 | 12830-12838 | official | 2012.07335 | title_snapshot |
10.1609/aaai.v35i14.17503 | Meta-Transfer Learning for Low-Resource Abstractive Summarization | https://ojs.aaai.org/index.php/AAAI/article/view/17503 | https://ojs.aaai.org/index.php/AAAI/article/download/17503/17310 | [
"Yi-Syuan Chen",
"Hong-Han Shuai"
] | Neural abstractive summarization has been studied in many pieces of literature and achieves great success with the aid of large corpora. However, when encountering novel tasks, one may not always benefit from transfer learning due to the domain shifting problem, and overfitting could happen without adequate labeled exa... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i14.17503 | 35 | 14 | 12692-12700 | official | 2102.09397 | title_snapshot |
10.1609/aaai.v35i14.17504 | Adaptive Prior-Dependent Correction Enhanced Reinforcement Learning for Natural Language Generation | https://ojs.aaai.org/index.php/AAAI/article/view/17504 | https://ojs.aaai.org/index.php/AAAI/article/download/17504/17311 | [
"Wei Cheng",
"Ziyan Luo",
"Qiyue Yin"
] | Natural language generation (NLG) is an important task with various applications like neural machine translation (NMT) and image captioning. Since deep-learning-based methods have issues of exposure bias and loss inconsistency, reinforcement learning (RL) is widely adopted in NLG tasks recently. But most RL-based metho... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i14.17504 | 35 | 14 | 12701-12709 | official | null | null |
10.1609/aaai.v35i14.17505 | How Linguistically Fair Are Multilingual Pre-Trained Language Models? | https://ojs.aaai.org/index.php/AAAI/article/view/17505 | https://ojs.aaai.org/index.php/AAAI/article/download/17505/17312 | [
"Monojit Choudhury",
"Amit Deshpande"
] | Massively multilingual pre-trained language models, such as mBERT and XLM-RoBERTa, have received significant attention in the recent NLP literature for their excellent capability towards crosslingual zero-shot transfer of NLP tasks. This is especially promising because a large number of languages have no or very little... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i14.17505 | 35 | 14 | 12710-12718 | official | null | null |
10.1609/aaai.v35i14.17506 | DirectQE: Direct Pretraining for Machine Translation Quality Estimation | https://ojs.aaai.org/index.php/AAAI/article/view/17506 | https://ojs.aaai.org/index.php/AAAI/article/download/17506/17313 | [
"Qu Cui",
"Shujian Huang",
"Jiahuan Li",
"Xiang Geng",
"Zaixiang Zheng",
"Guoping Huang",
"Jiajun Chen"
] | Machine Translation Quality Estimation (QE) is a task of predicting the quality of machine translations without relying on any reference. Recently, the predictor-estimator framework trains the predictor as a feature extractor, which leverages the extra parallel corpora without QE labels, achieving promising QE performa... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i14.17506 | 35 | 14 | 12719-12727 | official | 2105.07149 | title_snapshot |
10.1609/aaai.v35i14.17507 | We Can Explain Your Research in Layman's Terms: Towards Automating Science Journalism at Scale | https://ojs.aaai.org/index.php/AAAI/article/view/17507 | https://ojs.aaai.org/index.php/AAAI/article/download/17507/17314 | [
"Rumen Dangovski",
"Michelle Shen",
"Dawson Byrd",
"Li Jing",
"Desislava Tsvetkova",
"Preslav Nakov",
"Marin Soljačić"
] | We propose to study Automating Science Journalism (ASJ), the process of producing a layman's terms summary of a research article, as a new benchmark for long neural abstractive summarization and story generation. Automating science journalism is a challenging task as it requires paraphrasing complex scientific concepts... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i14.17507 | 35 | 14 | 12728-12737 | official | null | null |
10.1609/aaai.v35i14.17508 | Consecutive Decoding for Speech-to-text Translation | https://ojs.aaai.org/index.php/AAAI/article/view/17508 | https://ojs.aaai.org/index.php/AAAI/article/download/17508/17315 | [
"Qianqian Dong",
"Mingxuan Wang",
"Hao Zhou",
"Shuang Xu",
"Bo Xu",
"Lei Li"
] | Speech-to-text translation (ST), which directly translates the source language speech to the target language text, has attracted intensive attention recently. However, the combination of speech recognition and machine translation in a single model poses a heavy burden on the direct cross-modal cross-lingual mapping. To... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i14.17508 | 35 | 14 | 12738-12748 | official | 2009.09737 | title_snapshot |
10.1609/aaai.v35i14.17509 | Listen, Understand and Translate: Triple Supervision Decouples End-to-end Speech-to-text Translation | https://ojs.aaai.org/index.php/AAAI/article/view/17509 | https://ojs.aaai.org/index.php/AAAI/article/download/17509/17316 | [
"Qianqian Dong",
"Rong Ye",
"Mingxuan Wang",
"Hao Zhou",
"Shuang Xu",
"Bo Xu",
"Lei Li"
] | An end-to-end speech-to-text translation (ST) takes audio in a source language and outputs the text in a target language. Existing methods are limited by the amount of parallel corpus. Can we build a system to fully utilize signals in a parallel ST corpus? We are inspired by human understanding system which is composed... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i14.17509 | 35 | 14 | 12749-12759 | official | 2009.09704 | title_snapshot |
10.1609/aaai.v35i14.17510 | MultiTalk: A Highly-Branching Dialog Testbed for Diverse Conversations | https://ojs.aaai.org/index.php/AAAI/article/view/17510 | https://ojs.aaai.org/index.php/AAAI/article/download/17510/17317 | [
"Yao Dou",
"Maxwell Forbes",
"Ari Holtzman",
"Yejin Choi"
] | We study conversational dialog in which there are many possible responses to a given history. We present the MultiTalk Dataset, a corpus of over 320,000 sentences of written conversational dialog that balances a high branching factor (10) with several conversation turns (6) through selective branch continuation. We mak... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i14.17510 | 35 | 14 | 12760-12767 | official | 2102.01263 | title_snapshot |
10.1609/aaai.v35i14.17512 | FILTER: An Enhanced Fusion Method for Cross-lingual Language Understanding | https://ojs.aaai.org/index.php/AAAI/article/view/17512 | https://ojs.aaai.org/index.php/AAAI/article/download/17512/17319 | [
"Yuwei Fang",
"Shuohang Wang",
"Zhe Gan",
"Siqi Sun",
"Jingjing Liu"
] | Large-scale cross-lingual language models (LM), such as mBERT, Unicoder and XLM, have achieved great success in cross-lingual representation learning. However, when applied to zero-shot cross-lingual transfer tasks, most existing methods use only single-language input for LM finetuning, without leveraging the intrinsic... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i14.17512 | 35 | 14 | 12776-12784 | official | 2009.05166 | title_snapshot |
10.1609/aaai.v35i14.17513 | Rethinking Boundaries: End-To-End Recognition of Discontinuous Mentions with Pointer Networks | https://ojs.aaai.org/index.php/AAAI/article/view/17513 | https://ojs.aaai.org/index.php/AAAI/article/download/17513/17320 | [
"Hao Fei",
"Donghong Ji",
"Bobo Li",
"Yijiang Liu",
"Yafeng Ren",
"Fei Li"
] | A majority of research interests in irregular (e.g., nested or discontinuous) named entity recognition (NER) have been paid on nested entities, while discontinuous entities received limited attention. Existing work for discontinuous NER, however, either suffers from decoding ambiguity or predicting using token-level lo... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i14.17513 | 35 | 14 | 12785-12793 | official | null | null |
10.1609/aaai.v35i14.17514 | Encoder-Decoder Based Unified Semantic Role Labeling with Label-Aware Syntax | https://ojs.aaai.org/index.php/AAAI/article/view/17514 | https://ojs.aaai.org/index.php/AAAI/article/download/17514/17321 | [
"Hao Fei",
"Fei Li",
"Bobo Li",
"Donghong Ji"
] | Currently the unified semantic role labeling (SRL) that achieves predicate identification and argument role labeling in an end-to-end manner has received growing interests. Recent works show that leveraging the syntax knowledge significantly enhances the SRL performances. In this paper, we investigate a novel unified S... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i14.17514 | 35 | 14 | 12794-12802 | official | null | null |
10.1609/aaai.v35i14.17515 | End-to-end Semantic Role Labeling with Neural Transition-based Model | https://ojs.aaai.org/index.php/AAAI/article/view/17515 | https://ojs.aaai.org/index.php/AAAI/article/download/17515/17322 | [
"Hao Fei",
"Meishan Zhang",
"Bobo Li",
"Donghong Ji"
] | End-to-end semantic role labeling (SRL) has been received increasing interest. It performs the two subtasks of SRL: predicate identification and argument role labeling, jointly. Recent work is mostly focused on graph-based neural models, while the transition-based framework with neural networks which has been widely us... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i14.17515 | 35 | 14 | 12803-12811 | official | 2101.00394 | title_snapshot |
10.1609/aaai.v35i14.17516 | Multi-View Feature Representation for Dialogue Generation with Bidirectional Distillation | https://ojs.aaai.org/index.php/AAAI/article/view/17516 | https://ojs.aaai.org/index.php/AAAI/article/download/17516/17323 | [
"Shaoxiong Feng",
"Xuancheng Ren",
"Kan Li",
"Xu Sun"
] | Neural dialogue models suffer from low-quality responses when interacted in practice, demonstrating difficulty in generalization beyond training data. Recently, knowledge distillation has been used to successfully regularize the student by transferring knowledge from the teacher. However, the teacher and the student ar... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i14.17516 | 35 | 14 | 12812-12820 | official | 2102.10780 | title_snapshot |
10.1609/aaai.v35i14.17517 | More the Merrier: Towards Multi-Emotion and Intensity Controllable Response Generation | https://ojs.aaai.org/index.php/AAAI/article/view/17517 | https://ojs.aaai.org/index.php/AAAI/article/download/17517/17324 | [
"Mauajama Firdaus",
"Hardik Chauhan",
"Asif Ekbal",
"Pushpak Bhattacharyya"
] | The focus on conversational systems has recently shifted towards creating engaging agents by inculcating emotions into them. Human emotions are highly complex as humans can express multiple emotions with varying intensity in a single utterance, whereas the conversational agents convey only one emotion in their response... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i14.17517 | 35 | 14 | 12821-12829 | official | null | null |
10.1609/aaai.v35i14.17511 | Knowledge-aware Leap-LSTM: Integrating Prior Knowledge into Leap-LSTM towards Faster Long Text Classification | https://ojs.aaai.org/index.php/AAAI/article/view/17511 | https://ojs.aaai.org/index.php/AAAI/article/download/17511/17318 | [
"Jinhua Du",
"Yan Huang",
"Karo Moilanen"
] | While widely used in industry, recurrent neural networks (RNNs) are known to have deficiencies in dealing with long sequences (e.g. slow inference, vanishing gradients etc.). Recent research has attempted to accelerate RNN models by developing mechanisms to skip irrelevant words in input. Due to the lack of labelled da... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i14.17511 | 35 | 14 | 12768-12775 | official | null | null |
10.1609/aaai.v35i14.17492 | Learning to Rationalize for Nonmonotonic Reasoning with Distant Supervision | https://ojs.aaai.org/index.php/AAAI/article/view/17492 | https://ojs.aaai.org/index.php/AAAI/article/download/17492/17299 | [
"Faeze Brahman",
"Vered Shwartz",
"Rachel Rudinger",
"Yejin Choi"
] | The black-box nature of neural models has motivated a line of research that aims to generate natural language rationales to explain why a model made certain predictions. Such rationale generation models, to date, have been trained on dataset-specific crowdsourced rationales, but this approach is costly and is not gener... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i14.17492 | 35 | 14 | 12592-12601 | official | 2012.08012 | title_snapshot |
10.1609/aaai.v35i14.17491 | Multilingual Transfer Learning for QA using Translation as Data Augmentation | https://ojs.aaai.org/index.php/AAAI/article/view/17491 | https://ojs.aaai.org/index.php/AAAI/article/download/17491/17298 | [
"Mihaela Bornea",
"Lin Pan",
"Sara Rosenthal",
"Radu Florian",
"Avirup Sil"
] | Prior work on multilingual question answering has mostly focused on using large multilingual pre-trained language models (LM) to perform zero-shot language-wise learning: train a QA model on English and test on other languages. In this work, we explore strategies that improve cross-lingual transfer by bringing the mult... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i14.17491 | 35 | 14 | 12583-12591 | official | 2012.05958 | title_snapshot |
10.1609/aaai.v35i14.17490 | Benchmarking Knowledge-Enhanced Commonsense Question Answering via Knowledge-to-Text Transformation | https://ojs.aaai.org/index.php/AAAI/article/view/17490 | https://ojs.aaai.org/index.php/AAAI/article/download/17490/17297 | [
"Ning Bian",
"Xianpei Han",
"Bo Chen",
"Le Sun"
] | A fundamental ability of humans is to utilize commonsense knowledge in language understanding and question answering. In recent years, many knowledge-enhanced Commonsense Question Answering (CQA) approaches have been proposed. However, it remains unclear: (1) How far can we get by exploiting external knowledge for CQA?... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i14.17490 | 35 | 14 | 12574-12582 | official | 2101.00760 | title_snapshot |
10.1609/aaai.v35i14.17489 | One SPRING to Rule Them Both: Symmetric AMR Semantic Parsing and Generation without a Complex Pipeline | https://ojs.aaai.org/index.php/AAAI/article/view/17489 | https://ojs.aaai.org/index.php/AAAI/article/download/17489/17296 | [
"Michele Bevilacqua",
"Rexhina Blloshmi",
"Roberto Navigli"
] | In Text-to-AMR parsing, current state-of-the-art semantic parsers use cumbersome pipelines integrating several different modules or components, and exploit graph recategorization, i.e., a set of content-specific heuristics that are developed on the basis of the training set. However, the generalizability of graph recat... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i14.17489 | 35 | 14 | 12564-12573 | official | null | null |
10.1609/aaai.v35i14.17488 | Knowledge-driven Natural Language Understanding of English Text and its Applications | https://ojs.aaai.org/index.php/AAAI/article/view/17488 | https://ojs.aaai.org/index.php/AAAI/article/download/17488/17295 | [
"Kinjal Basu",
"Sarat Chandra Varanasi",
"Farhad Shakerin",
"Joaquín Arias",
"Gopal Gupta"
] | Understanding the meaning of a text is a fundamental challenge of natural language understanding (NLU) research. An ideal NLU system should process a language in a way that is not exclusive to a single task or a dataset. Keeping this in mind, we have introduced a novel knowledge driven semantic representation approach ... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i14.17488 | 35 | 14 | 12554-12563 | official | 2101.11707 | title_snapshot |
10.1609/aaai.v35i14.17487 | Contextualized Rewriting for Text Summarization | https://ojs.aaai.org/index.php/AAAI/article/view/17487 | https://ojs.aaai.org/index.php/AAAI/article/download/17487/17294 | [
"Guangsheng Bao",
"Yue Zhang"
] | Extractive summarization suffers from irrelevance, redundancy and incoherence. Existing work shows that abstractive rewriting for extractive summaries can improve the conciseness and readability. These rewriting systems consider extracted summaries as the only input, which is relatively focused but can lose important b... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i14.17487 | 35 | 14 | 12544-12553 | official | 2102.00385 | title_snapshot |
10.1609/aaai.v35i14.17486 | Learning to Copy Coherent Knowledge for Response Generation | https://ojs.aaai.org/index.php/AAAI/article/view/17486 | https://ojs.aaai.org/index.php/AAAI/article/download/17486/17293 | [
"Jiaqi Bai",
"Ze Yang",
"Xinnian Liang",
"Wei Wang",
"Zhoujun Li"
] | Knowledge-driven dialog has shown remarkable performance to alleviate the problem of generating uninformative responses in the dialog system. However, incorporating knowledge coherently and accurately into response generation is still far from being solved. Previous works dropped into the paradigm of non-goal-oriented ... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i14.17486 | 35 | 14 | 12535-12543 | official | null | null |
10.1609/aaai.v35i14.17485 | Segatron: Segment-Aware Transformer for Language Modeling and Understanding | https://ojs.aaai.org/index.php/AAAI/article/view/17485 | https://ojs.aaai.org/index.php/AAAI/article/download/17485/17292 | [
"He Bai",
"Peng Shi",
"Jimmy Lin",
"Yuqing Xie",
"Luchen Tan",
"Kun Xiong",
"Wen Gao",
"Ming Li"
] | Transformers are powerful for sequence modeling. Nearly all state-of-the-art language models and pre-trained language models are based on the Transformer architecture. However, it distinguishes sequential tokens only with the token position index. We hypothesize that better contextual representations can be generated f... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i14.17485 | 35 | 14 | 12526-12534 | official | 2004.14996 | title_snapshot |
10.1609/aaai.v35i14.17484 | Joint Semantic Analysis with Document-Level Cross-Task Coherence Rewards | https://ojs.aaai.org/index.php/AAAI/article/view/17484 | https://ojs.aaai.org/index.php/AAAI/article/download/17484/17291 | [
"Rahul Aralikatte",
"Mostafa Abdou",
"Heather C Lent",
"Daniel Hershcovich",
"Anders Søgaard"
] | Coreference resolution and semantic role labeling are NLP tasks that capture different aspects of semantics, indicating respectively, which expressions refer to the same entity, and what semantic roles expressions serve in the sentence. However, they are often closely interdependent, and both generally necessitate natu... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i14.17484 | 35 | 14 | 12516-12525 | official | 2010.05567 | title_snapshot |
10.1609/aaai.v35i14.17483 | Multi-Dimensional Explanation of Target Variables from Documents | https://ojs.aaai.org/index.php/AAAI/article/view/17483 | https://ojs.aaai.org/index.php/AAAI/article/download/17483/17290 | [
"Diego Antognini",
"Claudiu Musat",
"Boi Faltings"
] | Automated predictions require explanations to be interpretable by humans. Past work used attention and rationale mechanisms to find words that predict the target variable of a document. Often though, they result in a tradeoff between noisy explanations or a drop in accuracy. Furthermore, rationale methods cannot captur... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i14.17483 | 35 | 14 | 12507-12515 | official | 1909.11386 | title_snapshot |
10.1609/aaai.v35i14.17499 | A Lightweight Neural Model for Biomedical Entity Linking | https://ojs.aaai.org/index.php/AAAI/article/view/17499 | https://ojs.aaai.org/index.php/AAAI/article/download/17499/17306 | [
"Lihu Chen",
"Gaël Varoquaux",
"Fabian M. Suchanek"
] | Biomedical entity linking aims to map biomedical mentions, such as diseases and drugs, to standard entities in a given knowledge base. The specific challenge in this context is that the same biomedical entity can have a wide range of names, including synonyms, morphological variations, and names with different word ord... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i14.17499 | 35 | 14 | 12657-12665 | official | 2012.08844 | title_snapshot |
10.1609/aaai.v35i14.17493 | Brain Decoding Using fNIRS | https://ojs.aaai.org/index.php/AAAI/article/view/17493 | https://ojs.aaai.org/index.php/AAAI/article/download/17493/17300 | [
"Lu Cao",
"Dandan Huang",
"Yue Zhang",
"Xiaowei Jiang",
"Yanan Chen"
] | Brain activation can reflect semantic information elicited by natural words and concepts. Increasing research has been conducted on decoding such neural activation patterns using representational semantic models. However, prior work decoding semantic meaning from neurophysiological responses has been largely limited to... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i14.17493 | 35 | 14 | 12602-12611 | official | null | null |
10.1609/aaai.v35i14.17494 | Extracting Zero-shot Structured Information from Form-like Documents: Pretraining with Keys and Triggers | https://ojs.aaai.org/index.php/AAAI/article/view/17494 | https://ojs.aaai.org/index.php/AAAI/article/download/17494/17301 | [
"Rongyu Cao",
"Ping Luo"
] | In this paper, we revisit the problem of extracting the values of a given set of key fields from form-like documents. It is the vital step to support many downstream applications, such as knowledge base construction, question answering, document comprehension and so on. Previous studies ignore the semantics of the give... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i14.17494 | 35 | 14 | 12612-12620 | official | null | null |
10.1609/aaai.v35i14.17495 | Simple or Complex? Learning to Predict Readability of Bengali Texts | https://ojs.aaai.org/index.php/AAAI/article/view/17495 | https://ojs.aaai.org/index.php/AAAI/article/download/17495/17302 | [
"Susmoy Chakraborty",
"Mir Tafseer Nayeem",
"Wasi Uddin Ahmad"
] | Determining the readability of a text is the first step to its simplification. In this paper, we present a readability analysis tool capable of analyzing text written in the Bengali language to provide in-depth information on its readability and complexity. Despite being the 7th most spoken language in the world with 2... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i14.17495 | 35 | 14 | 12621-12629 | official | 2012.07701 | title_snapshot |
10.1609/aaai.v35i14.17496 | Lexically Constrained Neural Machine Translation with Explicit Alignment Guidance | https://ojs.aaai.org/index.php/AAAI/article/view/17496 | https://ojs.aaai.org/index.php/AAAI/article/download/17496/17303 | [
"Guanhua Chen",
"Yun Chen",
"Victor O.K. Li"
] | Lexically constrained neural machine translation (NMT), which leverages pre-specified translation to constrain NMT, has practical significance in interactive translation and NMT domain adaption. Previous work either modify the decoding algorithm or train the model on augmented dataset. These methods suffer from either ... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i14.17496 | 35 | 14 | 12630-12638 | official | null | null |
10.1609/aaai.v35i14.17497 | Aspect-Level Sentiment-Controllable Review Generation with Mutual Learning Framework | https://ojs.aaai.org/index.php/AAAI/article/view/17497 | https://ojs.aaai.org/index.php/AAAI/article/download/17497/17304 | [
"Huimin Chen",
"Yankai Lin",
"Fanchao Qi",
"Jinyi Hu",
"Peng Li",
"Jie Zhou",
"Maosong Sun"
] | Review generation, aiming to automatically generate review text according to the given information, is proposed to assist in the unappealing review writing. However, most of existing methods only consider the overall sentiments of reviews and cannot achieve aspect-level sentiment control. Even though some previous stud... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i14.17497 | 35 | 14 | 12639-12647 | official | null | null |
10.1609/aaai.v35i14.17498 | Weakly-Supervised Hierarchical Models for Predicting Persuasive Strategies in Good-faith Textual Requests | https://ojs.aaai.org/index.php/AAAI/article/view/17498 | https://ojs.aaai.org/index.php/AAAI/article/download/17498/17305 | [
"Jiaao Chen",
"Diyi Yang"
] | Modeling persuasive language has the potential to better facilitate our decision-making processes. Despite its importance, computational modeling of persuasion is still in its infancy, largely due to the lack of benchmark datasets that can provide quantitative labels of persuasive strategies to expedite this line of re... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i14.17498 | 35 | 14 | 12648-12656 | official | 2101.06351 | title_snapshot |
10.1609/aaai.v35i14.17500 | Bidirectional Machine Reading Comprehension for Aspect Sentiment Triplet Extraction | https://ojs.aaai.org/index.php/AAAI/article/view/17500 | https://ojs.aaai.org/index.php/AAAI/article/download/17500/17307 | [
"Shaowei Chen",
"Yu Wang",
"Jie Liu",
"Yuelin Wang"
] | Aspect sentiment triplet extraction (ASTE), which aims to identify aspects from review sentences along with their corresponding opinion expressions and sentiments, is an emerging task in fine-grained opinion mining. Since ASTE consists of multiple subtasks, including opinion entity extraction, relation detection, and s... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i14.17500 | 35 | 14 | 12666-12674 | official | 2103.07665 | title_snapshot |
10.1609/aaai.v35i14.17501 | Empower Distantly Supervised Relation Extraction with Collaborative Adversarial Training | https://ojs.aaai.org/index.php/AAAI/article/view/17501 | https://ojs.aaai.org/index.php/AAAI/article/download/17501/17308 | [
"Tao Chen",
"Haochen Shi",
"Liyuan Liu",
"Siliang Tang",
"Jian Shao",
"Zhigang Chen",
"Yueting Zhuang"
] | With recent advances in distantly supervised (DS) relation extraction (RE), considerable attention is attracted to leverage multi-instance learning (MIL) to distill high-quality supervision from the noisy DS. Here, we go beyond label noise and identify the key bottleneck of DS-MIL to be its low data utilization: as hig... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i14.17501 | 35 | 14 | 12675-12682 | official | 2106.10835 | title_snapshot |
10.1609/aaai.v35i14.17502 | Reasoning in Dialog: Improving Response Generation by Context Reading Comprehension | https://ojs.aaai.org/index.php/AAAI/article/view/17502 | https://ojs.aaai.org/index.php/AAAI/article/download/17502/17309 | [
"Xiuying Chen",
"Zhi Cui",
"Jiayi Zhang",
"Chen Wei",
"Jianwei Cui",
"Bin Wang",
"Dongyan Zhao",
"Rui Yan"
] | In multi-turn dialog, utterances do not always take the full form of sentences (Carbonell 1983), which naturally makes understanding the dialog context more difficult. However, it is essential to fully grasp the dialog context to generate a reasonable response. Hence, in this paper, we propose to improve the response g... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i14.17502 | 35 | 14 | 12683-12691 | official | 2012.07410 | title_snapshot |
10.1609/aaai.v35i14.17478 | GATE: Graph Attention Transformer Encoder for Cross-lingual Relation and Event Extraction | https://ojs.aaai.org/index.php/AAAI/article/view/17478 | https://ojs.aaai.org/index.php/AAAI/article/download/17478/17285 | [
"Wasi Uddin Ahmad",
"Nanyun Peng",
"Kai-Wei Chang"
] | Recent progress in cross-lingual relation and event extraction use graph convolutional networks (GCNs) with universal dependency parses to learn language-agnostic sentence representations such that models trained on one language can be applied to other languages. However, GCNs struggle to model words with long-range de... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i14.17478 | 35 | 14 | 12462-12470 | official | 2010.03009 | title_snapshot |
10.1609/aaai.v35i14.17482 | Enhancing Scientific Papers Summarization with Citation Graph | https://ojs.aaai.org/index.php/AAAI/article/view/17482 | https://ojs.aaai.org/index.php/AAAI/article/download/17482/17289 | [
"Chenxin An",
"Ming Zhong",
"Yiran Chen",
"Danqing Wang",
"Xipeng Qiu",
"Xuanjing Huang"
] | Previous work for text summarization in scientific domain mainly focused on the content of the input document, but seldom considering its citation network. However, scientific papers are full of uncommon domain-specific terms, making it almost impossible for the model to understand its true meaning without the help of ... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i14.17482 | 35 | 14 | 12498-12506 | official | 2104.03057 | title_snapshot |
10.1609/aaai.v35i14.17481 | Unsupervised Opinion Summarization with Content Planning | https://ojs.aaai.org/index.php/AAAI/article/view/17481 | https://ojs.aaai.org/index.php/AAAI/article/download/17481/17288 | [
"Reinald Kim Amplayo",
"Stefanos Angelidis",
"Mirella Lapata"
] | The recent success of deep learning techniques for abstractive summarization is predicated on the availability of large-scale datasets. When summarizing reviews (e.g., for products or movies), such training data is neither available nor can be easily sourced, motivating the development of methods which rely on syntheti... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i14.17481 | 35 | 14 | 12489-12497 | official | 2012.07808 | title_snapshot |
10.1609/aaai.v35i14.17480 | Segmentation of Tweets with URLs and its Applications to Sentiment Analysis | https://ojs.aaai.org/index.php/AAAI/article/view/17480 | https://ojs.aaai.org/index.php/AAAI/article/download/17480/17287 | [
"Abdullah Aljebreen",
"Weiyi Meng",
"Eduard Dragut"
] | An important means for disseminating information in social media platforms is by including URLs that point to external sources in user posts. In Twitter, we estimate that about 21% of the daily stream of English-language tweets contain URLs. We notice that NLP tools make little attempt at understanding the relationship... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i14.17480 | 35 | 14 | 12480-12488 | official | null | null |
10.1609/aaai.v35i14.17479 | Empirical Regularization for Synthetic Sentence Pairs in Unsupervised Neural Machine Translation | https://ojs.aaai.org/index.php/AAAI/article/view/17479 | https://ojs.aaai.org/index.php/AAAI/article/download/17479/17286 | [
"Xi Ai",
"Bin Fang"
] | UNMT tackles translation on monolingual corpora in two required languages. Since there is no explicitly cross-lingual signal, pre-training and synthetic sentence pairs are significant to the success of UNMT. In this work, we empirically study the core training procedure of UNMT to analyze the synthetic sentence pairs o... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i14.17479 | 35 | 14 | 12471-12479 | official | null | null |
10.1609/aaai.v35i15.17641 | Exploring Explainable Selection to Control Abstractive Summarization | https://ojs.aaai.org/index.php/AAAI/article/view/17641 | https://ojs.aaai.org/index.php/AAAI/article/download/17641/17448 | [
"Haonan Wang",
"Yang Gao",
"Yu Bai",
"Mirella Lapata",
"Heyan Huang"
] | Like humans, document summarization models can interpret a document’s contents in a number of ways. Unfortunately, the neural models of today are largely black boxes that provide little explanation of how or why they generated a summary in the way they did. Therefore, to begin prying open the black box and to inject a ... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i15.17641 | 35 | 15 | 13933-13941 | official | 2004.11779 | title_snapshot |
10.1609/aaai.v35i15.17623 | Learning from the Best: Rationalizing Predictions by Adversarial Information Calibration | https://ojs.aaai.org/index.php/AAAI/article/view/17623 | https://ojs.aaai.org/index.php/AAAI/article/download/17623/17430 | [
"Lei Sha",
"Oana-Maria Camburu",
"Thomas Lukasiewicz"
] | Explaining the predictions of AI models is paramount in safety-critical applications, such as in legal or medical domains. One form of explanation for a prediction is an extractive rationale, i.e., a subset of features of an instance that lead the model to give its prediction on the instance. Previous works on generati... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i15.17623 | 35 | 15 | 13771-13779 | official | 2012.08884 | title_judge |
10.1609/aaai.v35i15.17624 | Nutri-bullets: Summarizing Health Studies by Composing Segments | https://ojs.aaai.org/index.php/AAAI/article/view/17624 | https://ojs.aaai.org/index.php/AAAI/article/download/17624/17431 | [
"Darsh J Shah",
"Lili Yu",
"Tao Lei",
"Regina Barzilay"
] | We introduce Nutri-bullets, a multi-document summarization task for health and nutrition. First, we present two datasets of food and health summaries from multiple scientific studies. Furthermore, we propose a novel extract-compose model to solve the problem in the regime of limited parallel data. We explicitly select ... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i15.17624 | 35 | 15 | 13780-13788 | official | 2103.11921 | title_snapshot |
10.1609/aaai.v35i15.17625 | DialogXL: All-in-One XLNet for Multi-Party Conversation Emotion Recognition | https://ojs.aaai.org/index.php/AAAI/article/view/17625 | https://ojs.aaai.org/index.php/AAAI/article/download/17625/17432 | [
"Weizhou Shen",
"Junqing Chen",
"Xiaojun Quan",
"Zhixian Xie"
] | This paper presents our pioneering effort for emotion recognition in conversation (ERC) with pre-trained language models. Unlike regular documents, conversational utterances appear alternately from different parties and are usually organized as hierarchical structures in previous work. Such structures are not conducive... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i15.17625 | 35 | 15 | 13789-13797 | official | 2012.08695 | title_snapshot |
10.1609/aaai.v35i15.17626 | SongMASS: Automatic Song Writing with Pre-training and Alignment Constraint | https://ojs.aaai.org/index.php/AAAI/article/view/17626 | https://ojs.aaai.org/index.php/AAAI/article/download/17626/17433 | [
"Zhonghao Sheng",
"Kaitao Song",
"Xu Tan",
"Yi Ren",
"Wei Ye",
"Shikun Zhang",
"Tao Qin"
] | Automatic song writing aims to compose a song (lyric and/or melody) by machine, which is an interesting topic in both academia and industry. In automatic song writing, lyric-to-melody generation and melody-to-lyric generation are two important tasks, both of which usually suffer from the following challenges: 1) the pa... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i15.17626 | 35 | 15 | 13798-13805 | official | 2012.05168 | title_snapshot |
10.1609/aaai.v35i15.17627 | Learning Contextual Representations for Semantic Parsing with Generation-Augmented Pre-Training | https://ojs.aaai.org/index.php/AAAI/article/view/17627 | https://ojs.aaai.org/index.php/AAAI/article/download/17627/17434 | [
"Peng Shi",
"Patrick Ng",
"Zhiguo Wang",
"Henghui Zhu",
"Alexander Hanbo Li",
"Jun Wang",
"Cicero Nogueira dos Santos",
"Bing Xiang"
] | Most recently, there has been significant interest in learning contextual representations for various NLP tasks, by leveraging large scale text corpora to train powerful language models with self-supervised learning objectives, such as Masked Language Model (MLM). Based on a pilot study, we observe three issues of exis... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i15.17627 | 35 | 15 | 13806-13814 | official | 2012.10309 | title_snapshot |
10.1609/aaai.v35i15.17628 | A Simple and Effective Self-Supervised Contrastive Learning Framework for Aspect Detection | https://ojs.aaai.org/index.php/AAAI/article/view/17628 | https://ojs.aaai.org/index.php/AAAI/article/download/17628/17435 | [
"Tian Shi",
"Liuqing Li",
"Ping Wang",
"Chandan K. Reddy"
] | Unsupervised aspect detection (UAD) aims at automatically extracting interpretable aspects and identifying aspect-specific segments (such as sentences) from online reviews. However, recent deep learning based topic models, specifically aspect-based autoencoder, suffer from several problems such as extracting noisy aspe... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i15.17628 | 35 | 15 | 13815-13824 | official | 2009.09107 | title_snapshot |
10.1609/aaai.v35i15.17629 | Fact-Enhanced Synthetic News Generation | https://ojs.aaai.org/index.php/AAAI/article/view/17629 | https://ojs.aaai.org/index.php/AAAI/article/download/17629/17436 | [
"Kai Shu",
"Yichuan Li",
"Kaize Ding",
"Huan Liu"
] | The advanced text generation methods have witnessed great success in text summarization, language translation, and synthetic news generation. However, these techniques can be abused to generate disinformation and fake news. To better understand the potential threats of synthetic news, we develop a novel generation meth... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i15.17629 | 35 | 15 | 13825-13833 | official | 2012.04778 | title_snapshot |
10.1609/aaai.v35i15.17630 | Improving Commonsense Causal Reasoning by Adversarial Training and Data Augmentation | https://ojs.aaai.org/index.php/AAAI/article/view/17630 | https://ojs.aaai.org/index.php/AAAI/article/download/17630/17437 | [
"Ieva Staliūnaitė",
"Philip John Gorinski",
"Ignacio Iacobacci"
] | Determining the plausibility of causal relations between clauses is a commonsense reasoning task that requires complex inference ability. The general approach to this task is to train a large pretrained language model on a specific dataset. However, the available training data for the task is often scarce, which leads ... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i15.17630 | 35 | 15 | 13834-13842 | official | 2101.04966 | title_snapshot |
10.1609/aaai.v35i15.17631 | Re-TACRED: Addressing Shortcomings of the TACRED Dataset | https://ojs.aaai.org/index.php/AAAI/article/view/17631 | https://ojs.aaai.org/index.php/AAAI/article/download/17631/17438 | [
"George Stoica",
"Emmanouil Antonios Platanios",
"Barnabas Poczos"
] | TACRED is one of the largest and most widely used sentence-level relation extraction datasets. Proposed models that are evaluated using this dataset consistently set new state-of-the-art performance. However, they still exhibit large error rates despite leveraging external knowledge and unsupervised pretraining on larg... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i15.17631 | 35 | 15 | 13843-13850 | official | 2104.08398 | title_snapshot |
10.1609/aaai.v35i15.17633 | RpBERT: A Text-image Relation Propagation-based BERT Model for Multimodal NER | https://ojs.aaai.org/index.php/AAAI/article/view/17633 | https://ojs.aaai.org/index.php/AAAI/article/download/17633/17440 | [
"Lin Sun",
"Jiquan Wang",
"Kai Zhang",
"Yindu Su",
"Fangsheng Weng"
] | Recently multimodal named entity recognition (MNER) has utilized images to improve the accuracy of NER in tweets. However, most of the multimodal methods use attention mechanisms to extract visual clues regardless of whether the text and image are relevant. Practically, the irrelevant text-image pairs account for a lar... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i15.17633 | 35 | 15 | 13860-13868 | official | 2102.02967 | title_snapshot |
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