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.v35i16.17691 | Writing Polishment with Simile: Task, Dataset and A Neural Approach | https://ojs.aaai.org/index.php/AAAI/article/view/17691 | https://ojs.aaai.org/index.php/AAAI/article/download/17691/17498 | [
"Jiayi Zhang",
"Zhi Cui",
"Xiaoqiang Xia",
"Yalong Guo",
"Yanran Li",
"Chen Wei",
"Jianwei Cui"
] | A simile is a figure of speech that directly makes a comparison, showing similarities between two different things, e.g. ``Reading papers can be dull sometimes,like watching grass grow". Human writers often interpolate appropriate similes into proper locations of the plain text to vivify their writings. However, none o... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i16.17691 | 35 | 16 | 14383-14392 | official | 2012.08117 | title_snapshot |
10.1609/aaai.v35i16.17692 | Continuous Self-Attention Models with Neural ODE Networks | https://ojs.aaai.org/index.php/AAAI/article/view/17692 | https://ojs.aaai.org/index.php/AAAI/article/download/17692/17499 | [
"Jing Zhang",
"Peng Zhang",
"Baiwen Kong",
"Junqiu Wei",
"Xin Jiang"
] | Stacked self-attention models receive widespread attention, due to its ability of capturing global dependency among words. However, the stacking of many layers and components generates huge parameters, leading to low parameter efficiency. In response to this issue, we propose a lightweight architecture named Continuous... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i16.17692 | 35 | 16 | 14393-14401 | official | null | null |
10.1609/aaai.v35i16.17693 | TaLNet: Voice Reconstruction from Tongue and Lip Articulation with Transfer Learning from Text-to-Speech Synthesis | https://ojs.aaai.org/index.php/AAAI/article/view/17693 | https://ojs.aaai.org/index.php/AAAI/article/download/17693/17500 | [
"Jing-Xuan Zhang",
"Korin Richmond",
"Zhen-Hua Ling",
"Lirong Dai"
] | This paper presents TaLNet, a model for voice reconstruction with ultrasound tongue and optical lip videos as inputs. TaLNet is based on an encoder-decoder architecture. Separate encoders are dedicated to processing the tongue and lip data streams respectively. The decoder predicts acoustic features conditioned on enco... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i16.17693 | 35 | 16 | 14402-14410 | official | null | null |
10.1609/aaai.v35i16.17694 | Making the Relation Matters: Relation of Relation Learning Network for Sentence Semantic Matching | https://ojs.aaai.org/index.php/AAAI/article/view/17694 | https://ojs.aaai.org/index.php/AAAI/article/download/17694/17501 | [
"Kun Zhang",
"Le Wu",
"Guangyi Lv",
"Meng Wang",
"Enhong Chen",
"Shulan Ruan"
] | Sentence semantic matching is one of the fundamental tasks in natural language processing, which requires an agent to determine the semantic relation among input sentences. Recently, deep neural networks have achieved impressive performance in this area, especially BERT. Despite the effectiveness of these models, most ... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i16.17694 | 35 | 16 | 14411-14419 | official | 2012.08920 | title_judge |
10.1609/aaai.v35i16.17695 | MERL: Multimodal Event Representation Learning in Heterogeneous Embedding Spaces | https://ojs.aaai.org/index.php/AAAI/article/view/17695 | https://ojs.aaai.org/index.php/AAAI/article/download/17695/17502 | [
"Linhai Zhang",
"Deyu Zhou",
"Yulan He",
"Zeng Yang"
] | Previous work has shown the effectiveness of using event representations for tasks such as script event prediction and stock market prediction. It is however still challenging to learn the subtle semantic differences between events based solely on textual descriptions of events often represented as (subject, predicate,... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i16.17695 | 35 | 16 | 14420-14427 | official | null | null |
10.1609/aaai.v35i16.17696 | Future-Guided Incremental Transformer for Simultaneous Translation | https://ojs.aaai.org/index.php/AAAI/article/view/17696 | https://ojs.aaai.org/index.php/AAAI/article/download/17696/17503 | [
"Shaolei Zhang",
"Yang Feng",
"Liangyou Li"
] | Simultaneous translation (ST) starts translations synchronously while reading source sentences, and is used in many online scenarios. The previous wait-k policy is concise and achieved good results in ST. However, wait-k policy faces two weaknesses: low training speed caused by the recalculation of hidden states and la... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i16.17696 | 35 | 16 | 14428-14436 | official | 2012.12465 | title_snapshot |
10.1609/aaai.v35i16.17697 | Semantics-Aware Inferential Network for Natural Language Understanding | https://ojs.aaai.org/index.php/AAAI/article/view/17697 | https://ojs.aaai.org/index.php/AAAI/article/download/17697/17504 | [
"Shuiliang Zhang",
"Hai Zhao",
"Junru Zhou",
"Xi Zhou",
"Xiang Zhou"
] | For natural language understanding tasks, either machine reading comprehension or natural language inference, both semantics-aware and inference are favorable features of the concerned modeling for better understanding performance. Thus we propose a Semantics-Aware Inferential Network (SAIN) to meet such a motivation. ... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i16.17697 | 35 | 16 | 14437-14445 | official | 2004.13338 | title_snapshot |
10.1609/aaai.v35i16.17698 | Learning to Check Contract Inconsistencies | https://ojs.aaai.org/index.php/AAAI/article/view/17698 | https://ojs.aaai.org/index.php/AAAI/article/download/17698/17505 | [
"Shuo Zhang",
"Junzhou Zhao",
"Pinghui Wang",
"Nuo Xu",
"Yang Yang",
"Yiting Liu",
"Yi Huang",
"Junlan Feng"
] | Contract consistency is important in ensuring the legal validity of the contract. In many scenarios, a contract is written by filling the blanks in a precompiled form. Due to carelessness, two blanks that should be filled with the same (or different) content may be incorrectly filled with different (or same) content. T... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i16.17698 | 35 | 16 | 14446-14453 | official | 2012.08150 | title_snapshot |
10.1609/aaai.v35i16.17699 | Self-supervised Bilingual Syntactic Alignment for Neural Machine Translation | https://ojs.aaai.org/index.php/AAAI/article/view/17699 | https://ojs.aaai.org/index.php/AAAI/article/download/17699/17506 | [
"Tianfu Zhang",
"Heyan Huang",
"Chong Feng",
"Longbing Cao"
] | While various neural machine translation (NMT) methods have integrated mono-lingual syntax knowledge into the linguistic representation of sequence-to-sequence, no research is available on aligning the syntactic structures of target language with the corresponding source language syntactic structures. This work shows t... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i16.17699 | 35 | 16 | 14454-14462 | official | null | null |
10.1609/aaai.v35i16.17700 | Graph-Based Tri-Attention Network for Answer Ranking in CQA | https://ojs.aaai.org/index.php/AAAI/article/view/17700 | https://ojs.aaai.org/index.php/AAAI/article/download/17700/17507 | [
"Wei Zhang",
"Zeyuan Chen",
"Chao Dong",
"Wen Wang",
"Hongyuan Zha",
"Jianyong Wang"
] | In community-based question answering (CQA) platforms, automatic answer ranking for a given question is critical for finding potentially popular answers in early times. The mainstream approaches learn to generate answer ranking scores based on the matching degree between question and answer representations as well as t... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i16.17700 | 35 | 16 | 14463-14471 | official | 2103.03583 | title_snapshot |
10.1609/aaai.v35i16.17701 | Circles are like Ellipses, or Ellipses are like Circles? Measuring the Degree of Asymmetry of Static and Contextual Word Embeddings and the Implications to Representation Learning | https://ojs.aaai.org/index.php/AAAI/article/view/17701 | https://ojs.aaai.org/index.php/AAAI/article/download/17701/17508 | [
"Wei Zhang",
"Murray Campbell",
"Yang Yu",
"Sadhana Kumaravel"
] | Human judgments of word similarity have been a popular method of evaluating the quality of word embedding. But it fails to measure the geometry properties such as asymmetry. For example, it is more natural to say ``Ellipses are like Circles'' than ``Circles are like Ellipses''. Such asymmetry has been observed from the... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i16.17701 | 35 | 16 | 14472-14480 | official | 2012.01631 | title_judge |
10.1609/aaai.v35i16.17702 | Denoising Distantly Supervised Named Entity Recognition via a Hypergeometric Probabilistic Model | https://ojs.aaai.org/index.php/AAAI/article/view/17702 | https://ojs.aaai.org/index.php/AAAI/article/download/17702/17509 | [
"Wenkai Zhang",
"Hongyu Lin",
"Xianpei Han",
"Le Sun",
"Huidan Liu",
"Zhicheng Wei",
"Nicholas Yuan"
] | Denoising is the essential step for distant supervision based named entity recognition. Previous denoising methods are mostly based on instance-level confidence statistics, which ignore the variety of the underlying noise distribution on different datasets and entity types. This makes them difficult to be adapted to hi... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i16.17702 | 35 | 16 | 14481-14488 | official | 2106.09234 | title_snapshot |
10.1609/aaai.v35i16.17683 | Meta-Curriculum Learning for Domain Adaptation in Neural Machine Translation | https://ojs.aaai.org/index.php/AAAI/article/view/17683 | https://ojs.aaai.org/index.php/AAAI/article/download/17683/17490 | [
"Runzhe Zhan",
"Xuebo Liu",
"Derek F. Wong",
"Lidia S. Chao"
] | Meta-learning has been sufficiently validated to be beneficial for low-resource neural machine translation (NMT). However, we find that meta-trained NMT fails to improve the translation performance of the domain unseen at the meta-training stage. In this paper, we aim to alleviate this issue by proposing a novel meta-c... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i16.17683 | 35 | 16 | 14310-14318 | official | 2103.02262 | title_snapshot |
10.1609/aaai.v35i16.17682 | Probing Product Description Generation via Posterior Distillation | https://ojs.aaai.org/index.php/AAAI/article/view/17682 | https://ojs.aaai.org/index.php/AAAI/article/download/17682/17489 | [
"Haolan Zhan",
"Hainan Zhang",
"Hongshen Chen",
"Lei Shen",
"Zhuoye Ding",
"Yongjun Bao",
"Weipeng Yan",
"Yanyan Lan"
] | In product description generation (PDG), the user-cared aspect is critical for the recommendation system, which can not only improve user's experiences but also obtain more clicks. High-quality customer reviews can be considered as an ideal source to mine user-cared aspects. However, in reality, a large number of new p... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i16.17682 | 35 | 16 | 14301-14309 | official | 2103.01594 | title_snapshot |
10.1609/aaai.v35i16.17663 | Enabling Fast and Universal Audio Adversarial Attack Using Generative Model | https://ojs.aaai.org/index.php/AAAI/article/view/17663 | https://ojs.aaai.org/index.php/AAAI/article/download/17663/17470 | [
"Yi Xie",
"Zhuohang Li",
"Cong Shi",
"Jian Liu",
"Yingying Chen",
"Bo Yuan"
] | Recently, the vulnerability of deep neural network (DNN)-based audio systems to adversarial attacks has obtained increasing attention. However, the existing audio adversarial attacks allow the adversary to possess the entire user's audio input as well as granting sufficient time budget to generate the adversarial pertu... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i16.17663 | 35 | 16 | 14129-14137 | official | 2004.12261 | title_snapshot |
10.1609/aaai.v35i16.17664 | Nyströmformer: A Nyström-based Algorithm for Approximating Self-Attention | https://ojs.aaai.org/index.php/AAAI/article/view/17664 | https://ojs.aaai.org/index.php/AAAI/article/download/17664/17471 | [
"Yunyang Xiong",
"Zhanpeng Zeng",
"Rudrasis Chakraborty",
"Mingxing Tan",
"Glenn Fung",
"Yin Li",
"Vikas Singh"
] | Transformers have emerged as a powerful tool for a broad range of natural language processing tasks. A key component that drives the impressive performance of Transformers is the self-attention mechanism that encodes the influence or dependence of other tokens on each specific token. While beneficial, the quadratic com... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i16.17664 | 35 | 16 | 14138-14148 | official | 2102.03902 | title_snapshot |
10.1609/aaai.v35i16.17665 | Entity Structure Within and Throughout: Modeling Mention Dependencies for Document-Level Relation Extraction | https://ojs.aaai.org/index.php/AAAI/article/view/17665 | https://ojs.aaai.org/index.php/AAAI/article/download/17665/17472 | [
"Benfeng Xu",
"Quan Wang",
"Yajuan Lyu",
"Yong Zhu",
"Zhendong Mao"
] | Entities, as the essential elements in relation extraction tasks, exhibit certain structure. In this work, we formulate such entity structure as distinctive dependencies between mention pairs. We then propose SSAN, which incorporates these structural dependencies within the standard self-attention mechanism and through... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i16.17665 | 35 | 16 | 14149-14157 | official | 2102.10249 | title_snapshot |
10.1609/aaai.v35i16.17666 | Learning an Effective Context-Response Matching Model with Self-Supervised Tasks for Retrieval-based Dialogues | https://ojs.aaai.org/index.php/AAAI/article/view/17666 | https://ojs.aaai.org/index.php/AAAI/article/download/17666/17473 | [
"Ruijian Xu",
"Chongyang Tao",
"Daxin Jiang",
"Xueliang Zhao",
"Dongyan Zhao",
"Rui Yan"
] | Building an intelligent dialogue system with the ability to select a proper response according to a multi-turn context is a great challenging task. Existing studies focus on building a context-response matching model with various neural architectures or pretrained language models (PLMs) and typically learning with a si... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i16.17666 | 35 | 16 | 14158-14166 | official | 2009.06265 | title_snapshot |
10.1609/aaai.v35i16.17667 | Document-Level Relation Extraction with Reconstruction | https://ojs.aaai.org/index.php/AAAI/article/view/17667 | https://ojs.aaai.org/index.php/AAAI/article/download/17667/17474 | [
"Wang Xu",
"Kehai Chen",
"Tiejun Zhao"
] | In document-level relation extraction (DocRE), graph structure is generally used to encode relation information in the input document to classify the relation category between each entity pair, and has greatly advanced the DocRE task over the past several years. However, the learned graph representation universally mod... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i16.17667 | 35 | 16 | 14167-14175 | official | 2012.11384 | title_snapshot |
10.1609/aaai.v35i16.17668 | Topic-Aware Multi-turn Dialogue Modeling | https://ojs.aaai.org/index.php/AAAI/article/view/17668 | https://ojs.aaai.org/index.php/AAAI/article/download/17668/17475 | [
"Yi Xu",
"Hai Zhao",
"Zhuosheng Zhang"
] | In the retrieval-based multi-turn dialogue modeling, it remains a challenge to select the most appropriate response according to extracting salient features in context utterances. As a conversation goes on, topic shift at discourse-level naturally happens through the continuous multi-turn dialogue context. However, all... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i16.17668 | 35 | 16 | 14176-14184 | official | 2009.12539 | title_snapshot |
10.1609/aaai.v35i16.17669 | A Supervised Multi-Head Self-Attention Network for Nested Named Entity Recognition | https://ojs.aaai.org/index.php/AAAI/article/view/17669 | https://ojs.aaai.org/index.php/AAAI/article/download/17669/17476 | [
"Yongxiu Xu",
"Heyan Huang",
"Chong Feng",
"Yue Hu"
] | In recent years, researchers have shown an increased interest in recognizing the overlapping entities that have nested structures. However, most existing models ignore the semantic correlation between words under different entity types. Considering words in sentence play different roles under different entity types, we... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i16.17669 | 35 | 16 | 14185-14193 | official | null | null |
10.1609/aaai.v35i16.17670 | GDPNet: Refining Latent Multi-View Graph for Relation Extraction | https://ojs.aaai.org/index.php/AAAI/article/view/17670 | https://ojs.aaai.org/index.php/AAAI/article/download/17670/17477 | [
"Fuzhao Xue",
"Aixin Sun",
"Hao Zhang",
"Eng Siong Chng"
] | Relation Extraction (RE) is to predict the relation type of two entities that are mentioned in a piece of text, e.g., a sentence or a dialogue. When the given text is long, it is challenging to identify indicative words for the relation prediction. Recent advances on RE task are from BERT-based sequence modeling and gr... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i16.17670 | 35 | 16 | 14194-14202 | official | 2012.06780 | title_snapshot |
10.1609/aaai.v35i16.17671 | Human-Level Interpretable Learning for Aspect-Based Sentiment Analysis | https://ojs.aaai.org/index.php/AAAI/article/view/17671 | https://ojs.aaai.org/index.php/AAAI/article/download/17671/17478 | [
"Rohan K Yadav",
"Lei Jiao",
"Ole-Christoffer Granmo",
"Morten Goodwin"
] | This paper proposes human-interpretable learning of aspect-based sentiment analysis (ABSA), employing the recently introduced Tsetlin Machines (TMs). We attain interpretability by converting the intricate position-dependent textual semantics into binary form, mapping all the features into bag-of-words (BOWs). The binar... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i16.17671 | 35 | 16 | 14203-14212 | official | null | null |
10.1609/aaai.v35i16.17672 | Style-transfer and Paraphrase: Looking for a Sensible Semantic Similarity Metric | https://ojs.aaai.org/index.php/AAAI/article/view/17672 | https://ojs.aaai.org/index.php/AAAI/article/download/17672/17479 | [
"Ivan P. Yamshchikov",
"Viacheslav Shibaev",
"Nikolay Khlebnikov",
"Alexey Tikhonov"
] | The rapid development of such natural language processing tasks as style transfer, paraphrase, and machine translation often calls for the use of semantic similarity metrics. In recent years a lot of methods to measure the semantic similarity of two short texts were developed. This paper provides a comprehensive analys... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i16.17672 | 35 | 16 | 14213-14220 | official | 2004.05001 | title_snapshot |
10.1609/aaai.v35i16.17674 | UBAR: Towards Fully End-to-End Task-Oriented Dialog System with GPT-2 | https://ojs.aaai.org/index.php/AAAI/article/view/17674 | https://ojs.aaai.org/index.php/AAAI/article/download/17674/17481 | [
"Yunyi Yang",
"Yunhao Li",
"Xiaojun Quan"
] | This paper presents our task-oriented dialog system UBAR which models task-oriented dialogs on a dialog session level. Specifically, UBAR is acquired by fine-tuning the large pre-trained unidirectional language model GPT-2 on the sequence of the entire dialog session which is composed of user utterance, belief state, d... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i16.17674 | 35 | 16 | 14230-14238 | official | 2012.03539 | title_judge |
10.1609/aaai.v35i16.17673 | Multi-Document Transformer for Personality Detection | https://ojs.aaai.org/index.php/AAAI/article/view/17673 | https://ojs.aaai.org/index.php/AAAI/article/download/17673/17480 | [
"Feifan Yang",
"Xiaojun Quan",
"Yunyi Yang",
"Jianxing Yu"
] | Personality detection aims to identify the personality traits implied in social media posts. The core of this task is to put together information in multiple scattered posts to depict an overall personality profile for each user. Existing approaches either encode each post individually or assemble posts arbitrarily int... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i16.17673 | 35 | 16 | 14221-14229 | official | null | null |
10.1609/aaai.v35i16.17681 | What's the Best Place for an AI Conference, Vancouver or _______: Why Completing Comparative Questions is Difficult | https://ojs.aaai.org/index.php/AAAI/article/view/17681 | https://ojs.aaai.org/index.php/AAAI/article/download/17681/17488 | [
"Avishai Zagoury",
"Einat Minkov",
"Idan Szpektor",
"William W. Cohen"
] | Although large neural language models (LMs) like BERT can be finetuned to yield state-of-the-art results on many NLP tasks, it is often unclear what these models actually learn. Here we study using such LMs to fill in entities in human-authored comparative questions, like ``Which country is older, India or _____?''---i... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i16.17681 | 35 | 16 | 14292-14300 | official | 2104.01940 | title_snapshot |
10.1609/aaai.v35i16.17680 | Reinforced Multi-Teacher Selection for Knowledge Distillation | https://ojs.aaai.org/index.php/AAAI/article/view/17680 | https://ojs.aaai.org/index.php/AAAI/article/download/17680/17487 | [
"Fei Yuan",
"Linjun Shou",
"Jian Pei",
"Wutao Lin",
"Ming Gong",
"Yan Fu",
"Daxin Jiang"
] | In natural language processing (NLP) tasks, slow inference speed and huge footprints in GPU usage remain the bottleneck of applying pre-trained deep models in production. As a popular method for model compression, knowledge distillation transfers knowledge from one or multiple large (teacher) models to a small (student... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i16.17680 | 35 | 16 | 14284-14291 | official | 2012.06048 | title_snapshot |
10.1609/aaai.v35i16.17679 | Simpson's Bias in NLP Training | https://ojs.aaai.org/index.php/AAAI/article/view/17679 | https://ojs.aaai.org/index.php/AAAI/article/download/17679/17486 | [
"Fei Yuan",
"Longtu Zhang",
"Huang Bojun",
"Yaobo Liang"
] | In most machine learning tasks, we evaluate a model M on a given data population S by measuring a population-level metric F(S;M). Examples of such evaluation metric F include precision/recall for (binary) recognition, the F1 score for multi-class classification, and the BLEU metric for language generation. On the other... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i16.17679 | 35 | 16 | 14276-14283 | official | 2103.11795 | title_snapshot |
10.1609/aaai.v35i16.17678 | Unanswerable Question Correction in Question Answering over Personal Knowledge Base | https://ojs.aaai.org/index.php/AAAI/article/view/17678 | https://ojs.aaai.org/index.php/AAAI/article/download/17678/17485 | [
"An-Zi Yen",
"Hen-Hsen Huang",
"Hsin-Hsi Chen"
] | People often encounter situations where they need to recall past experiences from their daily life. In this paper, we aim to construct a question answering system that enables human to query their past experiences over personal knowledge base. Previous works on knowledge base question answering focus on finding answers... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i16.17678 | 35 | 16 | 14266-14275 | official | null | null |
10.1609/aaai.v35i16.17677 | Contrastive Triple Extraction with Generative Transformer | https://ojs.aaai.org/index.php/AAAI/article/view/17677 | https://ojs.aaai.org/index.php/AAAI/article/download/17677/17484 | [
"Hongbin Ye",
"Ningyu Zhang",
"Shumin Deng",
"Mosha Chen",
"Chuanqi Tan",
"Fei Huang",
"Huajun Chen"
] | Triple extraction is an essential task in information extraction for natural language processing and knowledge graph construction. In this paper, we revisit the end-to-end triple extraction task for sequence generation. Since generative triple extraction may struggle to capture long-term dependencies and generate unfai... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i16.17677 | 35 | 16 | 14257-14265 | official | 2009.06207 | title_snapshot |
10.1609/aaai.v35i16.17676 | Adversarial Language Games for Advanced Natural Language Intelligence | https://ojs.aaai.org/index.php/AAAI/article/view/17676 | https://ojs.aaai.org/index.php/AAAI/article/download/17676/17483 | [
"Yuan Yao",
"Haoxi Zhong",
"Zhengyan Zhang",
"Xu Han",
"Xiaozhi Wang",
"Kai Zhang",
"Chaojun Xiao",
"Guoyang Zeng",
"Zhiyuan Liu",
"Maosong Sun"
] | We study the problem of adversarial language games, in which multiple agents with conflicting goals compete with each other via natural language interactions. While adversarial language games are ubiquitous in human activities, little attention has been devoted to this field in natural language processing. In this work... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i16.17676 | 35 | 16 | 14248-14256 | official | 1911.01622 | title_snapshot |
10.1609/aaai.v35i16.17675 | Open Domain Dialogue Generation with Latent Images | https://ojs.aaai.org/index.php/AAAI/article/view/17675 | https://ojs.aaai.org/index.php/AAAI/article/download/17675/17482 | [
"Ze Yang",
"Wei Wu",
"Huang Hu",
"Can Xu",
"Wei Wang",
"Zhoujun Li"
] | We consider grounding open domain dialogues with images. Existing work assumes that both an image and a textual context are available, but image-grounded dialogues by nature are more difficult to obtain than textual dialogues. Thus, we propose learning a response generation model with both image-grounded dialogues and ... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i16.17675 | 35 | 16 | 14239-14247 | official | 2004.01981 | title_snapshot |
10.1609/aaai.v35i16.17643 | Effective Slot Filling via Weakly-Supervised Dual-Model Learning | https://ojs.aaai.org/index.php/AAAI/article/view/17643 | https://ojs.aaai.org/index.php/AAAI/article/download/17643/17450 | [
"Jue Wang",
"Ke Chen",
"Lidan Shou",
"Sai Wu",
"Gang Chen"
] | Slot filling is a challenging task in Spoken Language Understanding (SLU). Supervised methods usually require large amounts of annotation to maintain desirable performance. A solution to relieve the heavy dependency on labeled data is to employ bootstrapping, which leverages unlabeled data. However, bootstrapping is kn... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i16.17643 | 35 | 16 | 13952-13960 | official | null | null |
10.1609/aaai.v35i16.17644 | Tune-In: Training Under Negative Environments with Interference for Attention Networks Simulating Cocktail Party Effect | https://ojs.aaai.org/index.php/AAAI/article/view/17644 | https://ojs.aaai.org/index.php/AAAI/article/download/17644/17451 | [
"Jun Wang",
"Max W. Y. Lam",
"Dan Su",
"Dong Yu"
] | We study the cocktail party problem and propose a novel attention network called Tune-In, abbreviated for training under negative environments with interference. It firstly learns two separate spaces of speaker-knowledge and speech-stimuli based on a shared feature space, where a new block structure is designed as the ... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i16.17644 | 35 | 16 | 13961-13969 | official | 2103.01461 | title_snapshot |
10.1609/aaai.v35i16.17645 | Bridging the Domain Gap: Improve Informal Language Translation via Counterfactual Domain Adaptation | https://ojs.aaai.org/index.php/AAAI/article/view/17645 | https://ojs.aaai.org/index.php/AAAI/article/download/17645/17452 | [
"Ke Wang",
"Guandan Chen",
"Zhongqiang Huang",
"Xiaojun Wan",
"Fei Huang"
] | Despite the near-human performances already achieved on formal texts such as news articles, neural machine translation still has difficulty in dealing with "user-generated" texts that have diverse linguistic phenomena but lack large-scale high-quality parallel corpora. To address this problem, we propose a counterfactu... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i16.17645 | 35 | 16 | 13970-13978 | official | null | null |
10.1609/aaai.v35i16.17646 | Tracking Interaction States for Multi-Turn Text-to-SQL Semantic Parsing | https://ojs.aaai.org/index.php/AAAI/article/view/17646 | https://ojs.aaai.org/index.php/AAAI/article/download/17646/17453 | [
"Run-Ze Wang",
"Zhen-Hua Ling",
"Jingbo Zhou",
"Yu Hu"
] | The task of multi-turn text-to-SQL semantic parsing aims to translate natural language utterances in an interaction into SQL queries in order to answer them using a database which normally contains multiple table schemas. Previous studies on this task usually utilized contextual information to enrich utterance represen... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i16.17646 | 35 | 16 | 13979-13987 | official | 2012.04995 | title_snapshot |
10.1609/aaai.v35i16.17647 | Generating Diversified Comments via Reader-Aware Topic Modeling and Saliency Detection | https://ojs.aaai.org/index.php/AAAI/article/view/17647 | https://ojs.aaai.org/index.php/AAAI/article/download/17647/17454 | [
"Wei Wang",
"Piji Li",
"Hai-Tao Zheng"
] | Automatic comment generation is a special and challenging task to verify the model ability on news content comprehension and language generation. Comments not only convey salient and interesting information in news articles, but also imply various and different reader characteristics which we treat as the essential clu... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i16.17647 | 35 | 16 | 13988-13996 | official | 2102.06856 | title_snapshot |
10.1609/aaai.v35i16.17648 | Adversarial Training with Fast Gradient Projection Method against Synonym Substitution Based Text Attacks | https://ojs.aaai.org/index.php/AAAI/article/view/17648 | https://ojs.aaai.org/index.php/AAAI/article/download/17648/17455 | [
"Xiaosen Wang",
"Yichen Yang",
"Yihe Deng",
"Kun He"
] | Adversarial training is the most empirically successful approach in improving the robustness of deep neural networks for image classification. For text classification, however, existing synonym substitution based adversarial attacks are effective but not very efficient to be incorporated into practical text adversarial... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i16.17648 | 35 | 16 | 13997-14005 | official | 2008.03709 | title_snapshot |
10.1609/aaai.v35i16.17649 | NaturalConv: A Chinese Dialogue Dataset Towards Multi-turn Topic-driven Conversation | https://ojs.aaai.org/index.php/AAAI/article/view/17649 | https://ojs.aaai.org/index.php/AAAI/article/download/17649/17456 | [
"Xiaoyang Wang",
"Chen Li",
"Jianqiao Zhao",
"Dong Yu"
] | In this paper, we propose a Chinese multi-turn topic-driven conversation dataset, NaturalConv, which allows the participants to chat anything they want as long as any element from the topic is mentioned and the topic shift is smooth. Our corpus contains 19.9K conversations from six domains, and 400K utterances with an ... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i16.17649 | 35 | 16 | 14006-14014 | official | 2103.02548 | title_snapshot |
10.1609/aaai.v35i16.17650 | Code Completion by Modeling Flattened Abstract Syntax Trees as Graphs | https://ojs.aaai.org/index.php/AAAI/article/view/17650 | https://ojs.aaai.org/index.php/AAAI/article/download/17650/17457 | [
"Yanlin Wang",
"Hui Li"
] | Code completion has become an essential component of integrated development environments. Contemporary code completion methods rely on the abstract syntax tree (AST) to generate syntactically correct code. However, they cannot fully capture the sequential and repetitive patterns of writing code and the structural infor... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i16.17650 | 35 | 16 | 14015-14023 | official | 2103.09499 | title_snapshot |
10.1609/aaai.v35i16.17651 | Robustness to Spurious Correlations in Text Classification via Automatically Generated Counterfactuals | https://ojs.aaai.org/index.php/AAAI/article/view/17651 | https://ojs.aaai.org/index.php/AAAI/article/download/17651/17458 | [
"Zhao Wang",
"Aron Culotta"
] | Spurious correlations threaten the validity of statistical classifiers. While model accuracy may appear high when the test data is from the same distribution as the training data, it can quickly degrade when the test distribution changes. For example, it has been shown that classifiers perform poorly when humans make m... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i16.17651 | 35 | 16 | 14024-14031 | official | 2012.10040 | title_snapshot |
10.1609/aaai.v35i16.17652 | MLE-Guided Parameter Search for Task Loss Minimization in Neural Sequence Modeling | https://ojs.aaai.org/index.php/AAAI/article/view/17652 | https://ojs.aaai.org/index.php/AAAI/article/download/17652/17459 | [
"Sean Welleck",
"Kyunghyun Cho"
] | Neural autoregressive sequence models are used to generate sequences in a variety of natural language processing (NLP) tasks, where they are evaluated according to sequence-level task losses. These models are typically trained with maximum likelihood estimation, which ignores the task loss, yet empirically performs wel... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i16.17652 | 35 | 16 | 14032-14040 | official | 2006.03158 | title_snapshot |
10.1609/aaai.v35i16.17653 | Do Response Selection Models Really Know What’s Next? Utterance Manipulation Strategies for Multi-turn Response Selection | https://ojs.aaai.org/index.php/AAAI/article/view/17653 | https://ojs.aaai.org/index.php/AAAI/article/download/17653/17460 | [
"Taesun Whang",
"Dongyub Lee",
"Dongsuk Oh",
"Chanhee Lee",
"Kijong Han",
"Dong-hun Lee",
"Saebyeok Lee"
] | In this paper, we study the task of selecting the optimal response given a user and system utterance history in retrieval-based multi-turn dialog systems. Recently, pre-trained language models (e.g., BERT, RoBERTa, and ELECTRA) showed significant improvements in various natural language processing tasks. This and simil... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i16.17653 | 35 | 16 | 14041-14049 | official | 2009.04703 | title_snapshot |
10.1609/aaai.v35i16.17654 | On Scalar Embedding of Relative Positions in Attention Models | https://ojs.aaai.org/index.php/AAAI/article/view/17654 | https://ojs.aaai.org/index.php/AAAI/article/download/17654/17461 | [
"Junshuang Wu",
"Richong Zhang",
"Yongyi Mao",
"Junfan Chen"
] | Attention with positional encoding has been demonstrated as a powerful component in modern neural network models, such as transformers. However, why positional encoding works well in attention models remains largely unanswered. In this paper, we study the scalar relative positional encoding (SRPE) proposed in the T5 tr... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i16.17654 | 35 | 16 | 14050-14057 | official | null | null |
10.1609/aaai.v35i16.17655 | Evidence Inference Networks for Interpretable Claim Verification | https://ojs.aaai.org/index.php/AAAI/article/view/17655 | https://ojs.aaai.org/index.php/AAAI/article/download/17655/17462 | [
"Lianwei Wu",
"Yuan Rao",
"Ling Sun",
"Wangbo He"
] | Existing approaches construct appropriate interaction models to explore semantic conflicts between claims and relevant articles, which provides practical solutions for interpretable claim verification. However, these conflicts are not necessarily all about questioning the false part of claims, which makes considerable ... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i16.17655 | 35 | 16 | 14058-14066 | official | null | null |
10.1609/aaai.v35i16.17656 | TextGAIL: Generative Adversarial Imitation Learning for Text Generation | https://ojs.aaai.org/index.php/AAAI/article/view/17656 | https://ojs.aaai.org/index.php/AAAI/article/download/17656/17463 | [
"Qingyang Wu",
"Lei Li",
"Zhou Yu"
] | Generative Adversarial Networks (GANs) for text generation have recently received many criticisms, as they perform worse than their MLE counterparts. We suspect previous text GANs' inferior performance is due to the lack of a reliable guiding signal in their discriminators. To address this problem, we propose a generat... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i16.17656 | 35 | 16 | 14067-14075 | official | 2004.13796 | title_snapshot |
10.1609/aaai.v35i16.17657 | MELINDA: A Multimodal Dataset for Biomedical Experiment Method Classification | https://ojs.aaai.org/index.php/AAAI/article/view/17657 | https://ojs.aaai.org/index.php/AAAI/article/download/17657/17464 | [
"Te-Lin Wu",
"Shikhar Singh",
"Sayan Paul",
"Gully Burns",
"Nanyun Peng"
] | We introduce a new dataset, MELINDA, for Multimodal biomEdicaL experImeNt methoD clAssification. The dataset is collected in a fully automated distant supervision manner, where the labels are obtained from an existing curated database, and the actual contents are extracted from papers associated with each of the record... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i16.17657 | 35 | 16 | 14076-14084 | official | 2012.09216 | title_snapshot |
10.1609/aaai.v35i16.17658 | A Controllable Model of Grounded Response Generation | https://ojs.aaai.org/index.php/AAAI/article/view/17658 | https://ojs.aaai.org/index.php/AAAI/article/download/17658/17465 | [
"Zeqiu Wu",
"Michel Galley",
"Chris Brockett",
"Yizhe Zhang",
"Xiang Gao",
"Chris Quirk",
"Rik Koncel-Kedziorski",
"Jianfeng Gao",
"Hannaneh Hajishirzi",
"Mari Ostendorf",
"Bill Dolan"
] | Current end-to-end neural conversation models inherently lack the flexibility to impose semantic control in the response generation process, often resulting in uninteresting responses. Attempts to boost informativeness alone come at the expense of factual accuracy, as attested by pretrained language models' propensity ... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i16.17658 | 35 | 16 | 14085-14093 | official | 2005.00613 | title_snapshot |
10.1609/aaai.v35i16.17659 | Context-Guided BERT for Targeted Aspect-Based Sentiment Analysis | https://ojs.aaai.org/index.php/AAAI/article/view/17659 | https://ojs.aaai.org/index.php/AAAI/article/download/17659/17466 | [
"Zhengxuan Wu",
"Desmond C. Ong"
] | Aspect-based sentiment analysis (ABSA) and Targeted ASBA (TABSA) allow finer-grained inferences about sentiment to be drawn from the same text, depending on context. For example, a given text can have different targets (e.g., neighborhoods) and different aspects (e.g., price or safety), with different sentiment associa... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i16.17659 | 35 | 16 | 14094-14102 | official | 2010.07523 | title_snapshot |
10.1609/aaai.v35i16.17660 | Does Head Label Help for Long-Tailed Multi-Label Text Classification | https://ojs.aaai.org/index.php/AAAI/article/view/17660 | https://ojs.aaai.org/index.php/AAAI/article/download/17660/17467 | [
"Lin Xiao",
"Xiangliang Zhang",
"Liping Jing",
"Chi Huang",
"Mingyang Song"
] | Multi-label text classification (MLTC) aims to annotate documents with the most relevant labels from a number of candidate labels. In real applications, the distribution of label frequency often exhibits a long tail, i.e., a few labels are associated with a large number of documents (a.k.a. head labels), while a large ... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i16.17660 | 35 | 16 | 14103-14111 | official | 2101.09704 | title_snapshot |
10.1609/aaai.v35i16.17661 | Adversarial Meta Sampling for Multilingual Low-Resource Speech Recognition | https://ojs.aaai.org/index.php/AAAI/article/view/17661 | https://ojs.aaai.org/index.php/AAAI/article/download/17661/17468 | [
"Yubei Xiao",
"Ke Gong",
"Pan Zhou",
"Guolin Zheng",
"Xiaodan Liang",
"Liang Lin"
] | Low-resource automatic speech recognition (ASR) is challenging, as the low-resource target language data cannot well train an ASR model. To solve this issue, meta-learning formulates ASR for each source language into many small ASR tasks and meta-learns a model initialization on all tasks from different source language... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i16.17661 | 35 | 16 | 14112-14120 | official | 2012.11896 | title_snapshot |
10.1609/aaai.v35i16.17662 | Improving Tree-Structured Decoder Training for Code Generation via Mutual Learning | https://ojs.aaai.org/index.php/AAAI/article/view/17662 | https://ojs.aaai.org/index.php/AAAI/article/download/17662/17469 | [
"Binbin Xie",
"Jinsong Su",
"Yubin Ge",
"Xiang Li",
"Jianwei Cui",
"Junfeng Yao",
"Bin Wang"
] | Code generation aims to automatically generate a piece of code given an input natural language utterance. Currently, among dominant models, it is treated as a sequence-to-tree task, where a decoder outputs a sequence of actions corresponding to the pre-order traversal of an Abstract Syntax Tree. However, such a decoder... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i16.17662 | 35 | 16 | 14121-14128 | official | 2105.14796 | title_snapshot |
10.1609/aaai.v35i16.17642 | Encoding Syntactic Knowledge in Transformer Encoder for Intent Detection and Slot Filling | https://ojs.aaai.org/index.php/AAAI/article/view/17642 | https://ojs.aaai.org/index.php/AAAI/article/download/17642/17449 | [
"Jixuan Wang",
"Kai Wei",
"Martin Radfar",
"Weiwei Zhang",
"Clement Chung"
] | We propose a novel Transformer encoder-based architecture with syntactical knowledge encoded for intent detection and slot filling. Specifically, we encode syntactic knowledge into the Transformer encoder by jointly training it to predict syntactic parse ancestors and part-of-speech of each token via multi-task learnin... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i16.17642 | 35 | 16 | 13943-13951 | official | 2012.11689 | title_snapshot |
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