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.v35i15.17634 | Unsupervised Learning of Deterministic Dialogue Structure with Edge-Enhanced Graph Auto-Encoder | https://ojs.aaai.org/index.php/AAAI/article/view/17634 | https://ojs.aaai.org/index.php/AAAI/article/download/17634/17441 | [
"Yajing Sun",
"Yong Shan",
"Chengguang Tang",
"Yue Hu",
"Yinpei Dai",
"Jing Yu",
"Jian Sun",
"Fei Huang",
"Luo Si"
] | It is important for task-oriented dialogue systems to discover the dialogue structure (i.e. the general dialogue flow) from dialogue corpora automatically. Previous work models dialogue structure by extracting latent states for each utterance first and then calculating the transition probabilities among states. These t... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i15.17634 | 35 | 15 | 13869-13877 | official | null | null |
10.1609/aaai.v35i15.17635 | VisualMRC: Machine Reading Comprehension on Document Images | https://ojs.aaai.org/index.php/AAAI/article/view/17635 | https://ojs.aaai.org/index.php/AAAI/article/download/17635/17442 | [
"Ryota Tanaka",
"Kyosuke Nishida",
"Sen Yoshida"
] | Recent studies on machine reading comprehension have focused on text-level understanding but have not yet reached the level of human understanding of the visual layout and content of real-world documents. In this study, we introduce a new visual machine reading comprehension dataset, named VisualMRC, wherein given a qu... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i15.17635 | 35 | 15 | 13878-13888 | official | 2101.11272 | title_snapshot |
10.1609/aaai.v35i15.17636 | A Bidirectional Multi-paragraph Reading Model for Zero-shot Entity Linking | https://ojs.aaai.org/index.php/AAAI/article/view/17636 | https://ojs.aaai.org/index.php/AAAI/article/download/17636/17443 | [
"Hongyin Tang",
"Xingwu Sun",
"Beihong Jin",
"Fuzheng Zhang"
] | Recently, a zero-shot entity linking task is introduced to challenge the generalization ability of entity linking models. In this task, mentions must be linked to unseen entities and only the textual information is available. In order to make full use of the documents, previous work has proposed a BERT-based model whic... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i15.17636 | 35 | 15 | 13889-13897 | official | null | null |
10.1609/aaai.v35i15.17637 | Ideography Leads Us to the Field of Cognition: A Radical-Guided Associative Model for Chinese Text Classification | https://ojs.aaai.org/index.php/AAAI/article/view/17637 | https://ojs.aaai.org/index.php/AAAI/article/download/17637/17444 | [
"Hanqing Tao",
"Shiwei Tong",
"Kun Zhang",
"Tong Xu",
"Qi Liu",
"Enhong Chen",
"Min Hou"
] | Cognitive psychology research shows that humans have the instinct for abstract thinking, where association plays an essential role in language comprehension. Especially for Chinese, its ideographic writing system allows radicals to trigger semantic association without the need of phonetics. In fact, subconsciously usin... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i15.17637 | 35 | 15 | 13898-13906 | official | null | null |
10.1609/aaai.v35i15.17638 | Learning from My Friends: Few-Shot Personalized Conversation Systems via Social Networks | https://ojs.aaai.org/index.php/AAAI/article/view/17638 | https://ojs.aaai.org/index.php/AAAI/article/download/17638/17445 | [
"Zhiliang Tian",
"Wei Bi",
"Zihan Zhang",
"Dongkyu Lee",
"Yiping Song",
"Nevin L. Zhang"
] | Personalized conversation models (PCMs) generate responses according to speaker preferences. Existing personalized conversation tasks typically require models to extract speaker preferences from user descriptions or their conversation histories, which are scarce for newcomers and inactive users. In this paper, we propo... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i15.17638 | 35 | 15 | 13907-13915 | official | 2105.10323 | title_snapshot |
10.1609/aaai.v35i15.17639 | FL-MSRE: A Few-Shot Learning based Approach to Multimodal Social Relation Extraction | https://ojs.aaai.org/index.php/AAAI/article/view/17639 | https://ojs.aaai.org/index.php/AAAI/article/download/17639/17446 | [
"Hai Wan",
"Manrong Zhang",
"Jianfeng Du",
"Ziling Huang",
"Yufei Yang",
"Jeff Z. Pan"
] | Social relation extraction (SRE for short), which aims to infer the social relation between two people in daily life, has been demonstrated to be of great value in reality. Existing methods for SRE consider extracting social relation only from unimodal information such as text or image, ignoring the high coupling of mu... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i15.17639 | 35 | 15 | 13916-13923 | official | null | null |
10.1609/aaai.v35i15.17640 | KEML: A Knowledge-Enriched Meta-Learning Framework for Lexical Relation Classification | https://ojs.aaai.org/index.php/AAAI/article/view/17640 | https://ojs.aaai.org/index.php/AAAI/article/download/17640/17447 | [
"Chengyu Wang",
"Minghui Qiu",
"Jun Huang",
"Xiaofeng He"
] | Lexical relations describe how concepts are semantically related, in the form of relation triples. The accurate prediction of lexical relations between concepts is challenging, due to the sparsity of patterns indicating the existence of such relations. We propose the Knowledge-Enriched Meta-Learning (KEML) framework to... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i15.17640 | 35 | 15 | 13924-13932 | official | 2002.10903 | title_snapshot |
10.1609/aaai.v35i15.17632 | Progressive Multi-task Learning with Controlled Information Flow for Joint Entity and Relation Extraction | https://ojs.aaai.org/index.php/AAAI/article/view/17632 | https://ojs.aaai.org/index.php/AAAI/article/download/17632/17439 | [
"Kai Sun",
"Richong Zhang",
"Samuel Mensah",
"Yongyi Mao",
"Xudong Liu"
] | Multitask learning has shown promising performance in learning multiple related tasks simultaneously, and variants of model architectures have been proposed, especially for supervised classification problems. One goal of multitask learning is to extract a good representation that sufficiently captures the relevant part... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i15.17632 | 35 | 15 | 13851-13859 | official | null | null |
10.1609/aaai.v35i15.17612 | Revisiting Mahalanobis Distance for Transformer-Based Out-of-Domain Detection | https://ojs.aaai.org/index.php/AAAI/article/view/17612 | https://ojs.aaai.org/index.php/AAAI/article/download/17612/17419 | [
"Alexander Podolskiy",
"Dmitry Lipin",
"Andrey Bout",
"Ekaterina Artemova",
"Irina Piontkovskaya"
] | Real-life applications, heavily relying on machine learning, such as dialog systems, demand for out-of-domain detection methods. Intent classification models should be equipped with a mechanism to distinguish seen intents from unseen ones so that the dialog agent is capable of rejecting the latter and avoiding undesire... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i15.17612 | 35 | 15 | 13675-13682 | official | 2101.03778 | title_snapshot |
10.1609/aaai.v35i15.17611 | Data Augmentation for Abstractive Query-Focused Multi-Document Summarization | https://ojs.aaai.org/index.php/AAAI/article/view/17611 | https://ojs.aaai.org/index.php/AAAI/article/download/17611/17418 | [
"Ramakanth Pasunuru",
"Asli Celikyilmaz",
"Michel Galley",
"Chenyan Xiong",
"Yizhe Zhang",
"Mohit Bansal",
"Jianfeng Gao"
] | The progress in Query-focused Multi-Document Summarization (QMDS) has been limited by the lack of sufficient largescale high-quality training datasets. We present two QMDS training datasets, which we construct using two data augmentation methods: (1) transferring the commonly used single-document CNN/Daily Mail summari... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i15.17611 | 35 | 15 | 13666-13674 | official | 2103.01863 | title_snapshot |
10.1609/aaai.v35i15.17610 | ALP-KD: Attention-Based Layer Projection for Knowledge Distillation | https://ojs.aaai.org/index.php/AAAI/article/view/17610 | https://ojs.aaai.org/index.php/AAAI/article/download/17610/17417 | [
"Peyman Passban",
"Yimeng Wu",
"Mehdi Rezagholizadeh",
"Qun Liu"
] | Knowledge distillation is considered as a training and compression strategy in which two neural networks, namely a teacher and a student, are coupled together during training. The teacher network is supposed to be a trustworthy predictor and the student tries to mimic its predictions. Usually, a student with a lighter ... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i15.17610 | 35 | 15 | 13657-13665 | official | 2012.14022 | title_snapshot |
10.1609/aaai.v35i15.17609 | XL-WSD: An Extra-Large and Cross-Lingual Evaluation Framework for Word Sense Disambiguation | https://ojs.aaai.org/index.php/AAAI/article/view/17609 | https://ojs.aaai.org/index.php/AAAI/article/download/17609/17416 | [
"Tommaso Pasini",
"Alessandro Raganato",
"Roberto Navigli"
] | Transformer-based architectures brought a breeze of change to Word Sense Disambiguation (WSD), improving models' performances by a large margin. The fast development of new approaches has been further encouraged by a well-framed evaluation suite for English, which has allowed their performances to be kept track of and ... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i15.17609 | 35 | 15 | 13648-13656 | official | null | null |
10.1609/aaai.v35i15.17608 | On the Softmax Bottleneck of Recurrent Language Models | https://ojs.aaai.org/index.php/AAAI/article/view/17608 | https://ojs.aaai.org/index.php/AAAI/article/download/17608/17415 | [
"Dwarak Govind Parthiban",
"Yongyi Mao",
"Diana Inkpen"
] | Recent research has pointed to a limitation of word-level neural language models with softmax outputs. This limitation, known as the softmax bottleneck refers to the inability of these models to produce high-rank log probability (log P) matrices. Various solutions have been proposed to break this bottleneck, including ... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i15.17608 | 35 | 15 | 13640-13647 | official | null | null |
10.1609/aaai.v35i15.17607 | Movie Summarization via Sparse Graph Construction | https://ojs.aaai.org/index.php/AAAI/article/view/17607 | https://ojs.aaai.org/index.php/AAAI/article/download/17607/17414 | [
"Pinelopi Papalampidi",
"Frank Keller",
"Mirella Lapata"
] | We summarize full-length movies by creating shorter videos containing their most informative scenes. We explore the hypothesis that a summary can be created by assembling scenes which are turning points (TPs), i.e., key events in a movie that describe its storyline. We propose a model that identifies TP scenes by build... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i15.17607 | 35 | 15 | 13631-13639 | official | 2012.07536 | title_snapshot |
10.1609/aaai.v35i15.17606 | Copy That! Editing Sequences by Copying Spans | https://ojs.aaai.org/index.php/AAAI/article/view/17606 | https://ojs.aaai.org/index.php/AAAI/article/download/17606/17413 | [
"Sheena Panthaplackel",
"Miltiadis Allamanis",
"Marc Brockschmidt"
] | Neural sequence-to-sequence models are finding increasing use in editing of documents, for example in correcting a text document or repairing source code. In this paper, we argue that common seq2seq models (with a facility to copy single tokens) are not a natural fit for such tasks, as they have to explicitly copy each... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i15.17606 | 35 | 15 | 13622-13630 | official | 2006.04771 | title_snapshot |
10.1609/aaai.v35i15.17605 | The Heads Hypothesis: A Unifying Statistical Approach Towards Understanding Multi-Headed Attention in BERT | https://ojs.aaai.org/index.php/AAAI/article/view/17605 | https://ojs.aaai.org/index.php/AAAI/article/download/17605/17412 | [
"Madhura Pande",
"Aakriti Budhraja",
"Preksha Nema",
"Pratyush Kumar",
"Mitesh M. Khapra"
] | Multi-headed attention heads are a mainstay in transformer-based models. Different methods have been proposed to classify the role of each attention head based on the relations between tokens which have high pair-wise attention. These roles include syntactic (tokens with some syntactic relation), local (nearby tokens),... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i15.17605 | 35 | 15 | 13613-13621 | official | 2101.09115 | title_snapshot |
10.1609/aaai.v35i15.17604 | Dialog Policy Learning for Joint Clarification and Active Learning Queries | https://ojs.aaai.org/index.php/AAAI/article/view/17604 | https://ojs.aaai.org/index.php/AAAI/article/download/17604/17411 | [
"Aishwarya Padmakumar",
"Raymond J. Mooney"
] | Intelligent systems need to be able to recover from mistakes, resolve uncertainty, and adapt to novel concepts not seen during training. Dialog interaction can enable this by the use of clarifications for correction and resolving uncertainty, and active learning queries to learn new concepts encountered during operatio... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i15.17604 | 35 | 15 | 13604-13612 | official | 2006.05456 | title_snapshot |
10.1609/aaai.v35i15.17603 | Knowledge-aware Named Entity Recognition with Alleviating Heterogeneity | https://ojs.aaai.org/index.php/AAAI/article/view/17603 | https://ojs.aaai.org/index.php/AAAI/article/download/17603/17410 | [
"Binling Nie",
"Ruixue Ding",
"Pengjun Xie",
"Fei Huang",
"Chen Qian",
"Luo Si"
] | Named Entity Recognition (NER) is a fundamental and important research topic for many downstream NLP tasks, aiming at detecting and classifying named entities (NEs) mentioned in unstructured text into pre-defined categories. Learning from labeled data only is far from enough when it comes to domain-specific or temporal... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i15.17603 | 35 | 15 | 13595-13603 | official | null | null |
10.1609/aaai.v35i15.17622 | Semantics Altering Modifications for Evaluating Comprehension in Machine Reading | https://ojs.aaai.org/index.php/AAAI/article/view/17622 | https://ojs.aaai.org/index.php/AAAI/article/download/17622/17429 | [
"Viktor Schlegel",
"Goran Nenadic",
"Riza Batista-Navarro"
] | Advances in NLP have yielded impressive results for the task of machine reading comprehension (MRC), with approaches having been reported to achieve performance comparable to that of humans. In this paper, we investigate whether state-of-the-art MRC models are able to correctly process Semantics Altering Modifications ... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i15.17622 | 35 | 15 | 13762-13770 | official | 2012.04056 | title_snapshot |
10.1609/aaai.v35i15.17613 | Conceptualized and Contextualized Gaussian Embedding | https://ojs.aaai.org/index.php/AAAI/article/view/17613 | https://ojs.aaai.org/index.php/AAAI/article/download/17613/17420 | [
"Chen Qian",
"Fuli Feng",
"Lijie Wen",
"Tat-Seng Chua"
] | Word embedding can represent a word as a point vector or a Gaussian distribution in high-dimensional spaces. Gaussian distribution is innately more expressive than point vector owing to the ability to additionally capture semantic uncertainties of words, and thus can express asymmetric relations among words more natura... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i15.17613 | 35 | 15 | 13683-13691 | official | null | null |
10.1609/aaai.v35i15.17614 | A Student-Teacher Architecture for Dialog Domain Adaptation Under the Meta-Learning Setting | https://ojs.aaai.org/index.php/AAAI/article/view/17614 | https://ojs.aaai.org/index.php/AAAI/article/download/17614/17421 | [
"Kun Qian",
"Wei Wei",
"Zhou Yu"
] | Numerous new dialog domains are being created every day while collecting data for these domains is extremely costly since it involves human interactions. Therefore, it is essential to develop algorithms that can adapt to different domains efficiently when building data-driven dialog models. Most recent research on doma... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i15.17614 | 35 | 15 | 13692-13700 | official | 2104.02689 | title_snapshot |
10.1609/aaai.v35i15.17615 | Exploring Auxiliary Reasoning Tasks for Task-oriented Dialog Systems with Meta Cooperative Learning | https://ojs.aaai.org/index.php/AAAI/article/view/17615 | https://ojs.aaai.org/index.php/AAAI/article/download/17615/17422 | [
"Bowen Qin",
"Min Yang",
"Lidong Bing",
"Qingshan Jiang",
"Chengming Li",
"Ruifeng Xu"
] | In this paper, we propose a Meta Cooperative Learning (MCL) framework for task-oriented dialog systems (TDSs). Our model consists of an auxiliary KB reasoning task for learning meta KB knowledge, an auxiliary dialogue reasoning task for learning dialogue patterns, and a TDS task (primary task) that aims at not only ret... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i15.17615 | 35 | 15 | 13701-13708 | official | null | null |
10.1609/aaai.v35i15.17616 | Co-GAT: A Co-Interactive Graph Attention Network for Joint Dialog Act Recognition and Sentiment Classification | https://ojs.aaai.org/index.php/AAAI/article/view/17616 | https://ojs.aaai.org/index.php/AAAI/article/download/17616/17423 | [
"Libo Qin",
"Zhouyang Li",
"Wanxiang Che",
"Minheng Ni",
"Ting Liu"
] | In a dialog system, dialog act recognition and sentiment classification are two correlative tasks to capture speakers’ intentions, where dialog act and sentiment can indicate the explicit and the implicit intentions separately. The dialog context information (contextual information) and the mutual interaction informati... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i15.17616 | 35 | 15 | 13709-13717 | official | 2012.13260 | title_snapshot |
10.1609/aaai.v35i15.17617 | Reinforced History Backtracking for Conversational Question Answering | https://ojs.aaai.org/index.php/AAAI/article/view/17617 | https://ojs.aaai.org/index.php/AAAI/article/download/17617/17424 | [
"Minghui Qiu",
"Xinjing Huang",
"Cen Chen",
"Feng Ji",
"Chen Qu",
"Wei Wei",
"Jun Huang",
"Yin Zhang"
] | To model the context history in multi-turn conversations has become a critical step towards a better understanding of the user query in question answering systems. To utilize the context history, most existing studies treat the whole context as input, which will inevitably face the following two challenges. First, mode... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i15.17617 | 35 | 15 | 13718-13726 | official | null | null |
10.1609/aaai.v35i15.17618 | Guiding Non-Autoregressive Neural Machine Translation Decoding with Reordering Information | https://ojs.aaai.org/index.php/AAAI/article/view/17618 | https://ojs.aaai.org/index.php/AAAI/article/download/17618/17425 | [
"Qiu Ran",
"Yankai Lin",
"Peng Li",
"Jie Zhou"
] | Non-autoregressive neural machine translation (NAT) generates each target word in parallel and has achieved promising inference acceleration. However, existing NAT models still have a big gap in translation quality compared to autoregressive neural machine translation models due to the multimodality problem: the target... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i15.17618 | 35 | 15 | 13727-13735 | official | 1911.02215 | title_snapshot |
10.1609/aaai.v35i15.17619 | Towards Semantics-Enhanced Pre-Training: Can Lexicon Definitions Help Learning Sentence Meanings? | https://ojs.aaai.org/index.php/AAAI/article/view/17619 | https://ojs.aaai.org/index.php/AAAI/article/download/17619/17426 | [
"Xuancheng Ren",
"Xu Sun",
"Houfeng Wang",
"Qun Liu"
] | Self-supervised pre-training techniques, albeit relying on large amounts of text, have enabled rapid growth in learning language representations for natural language understanding. However, as radically empirical models on sentences, they are subject to the input data distribution, inevitably incorporating data bias an... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i15.17619 | 35 | 15 | 13736-13744 | official | null | null |
10.1609/aaai.v35i15.17620 | Automated Cross-prompt Scoring of Essay Traits | https://ojs.aaai.org/index.php/AAAI/article/view/17620 | https://ojs.aaai.org/index.php/AAAI/article/download/17620/17427 | [
"Robert Ridley",
"Liang He",
"Xin-yu Dai",
"Shujian Huang",
"Jiajun Chen"
] | The majority of current research in Automated Essay Scoring (AES) focuses on prompt-specific scoring of either the overall quality of an essay or the quality with regards to certain traits. In real-world applications obtaining labelled data for a target essay prompt is often expensive or unfeasible, requiring the AES s... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i15.17620 | 35 | 15 | 13745-13753 | official | null | null |
10.1609/aaai.v35i15.17621 | Exploring Transfer Learning For End-to-End Spoken Language Understanding | https://ojs.aaai.org/index.php/AAAI/article/view/17621 | https://ojs.aaai.org/index.php/AAAI/article/download/17621/17428 | [
"Subendhu Rongali",
"Beiye Liu",
"Liwei Cai",
"Konstantine Arkoudas",
"Chengwei Su",
"Wael Hamza"
] | Voice Assistants such as Alexa, Siri, and Google Assistant typically use a two-stage Spoken Language Understanding pipeline; first, an Automatic Speech Recognition (ASR) component to process customer speech and generate text transcriptions, followed by a Natural Language Understanding (NLU) component to map transcripti... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i15.17621 | 35 | 15 | 13754-13761 | official | 2012.08549 | title_snapshot |
10.1609/aaai.v35i15.17601 | MASKER: Masked Keyword Regularization for Reliable Text Classification | https://ojs.aaai.org/index.php/AAAI/article/view/17601 | https://ojs.aaai.org/index.php/AAAI/article/download/17601/17408 | [
"Seung Jun Moon",
"Sangwoo Mo",
"Kimin Lee",
"Jaeho Lee",
"Jinwoo Shin"
] | Pre-trained language models have achieved state-of-the-art accuracies on various text classification tasks, e.g., sentiment analysis, natural language inference, and semantic textual similarity. However, the reliability of the fine-tuned text classifiers is an often underlooked performance criterion. For instance, one ... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i15.17601 | 35 | 15 | 13578-13586 | official | 2012.09392 | title_snapshot |
10.1609/aaai.v35i15.17602 | Disentangled Motif-aware Graph Learning for Phrase Grounding | https://ojs.aaai.org/index.php/AAAI/article/view/17602 | https://ojs.aaai.org/index.php/AAAI/article/download/17602/17409 | [
"Zongshen Mu",
"Siliang Tang",
"Jie Tan",
"Qiang Yu",
"Yueting Zhuang"
] | In this paper, we propose a novel graph learning framework for phrase grounding in the image. Developing from the sequential to the dense graph model, existing works capture coarse-grained context but fail to distinguish the diversity of context among phrases and image regions. In contrast, we pay special attention to ... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i15.17602 | 35 | 15 | 13587-13594 | official | 2104.06008 | title_snapshot |
10.1609/aaai.v35i15.17600 | Continual Learning for Named Entity Recognition | https://ojs.aaai.org/index.php/AAAI/article/view/17600 | https://ojs.aaai.org/index.php/AAAI/article/download/17600/17407 | [
"Natawut Monaikul",
"Giuseppe Castellucci",
"Simone Filice",
"Oleg Rokhlenko"
] | Named Entity Recognition (NER) is a vital task in various NLP applications. However, in many real-world scenarios (e.g., voice-enabled assistants) new named entities are frequently introduced, entailing re-training NER models to support these new entities. Re-annotating the original training data for the new entities c... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i15.17600 | 35 | 15 | 13570-13577 | official | null | null |
10.1609/aaai.v35i15.17583 | Towards Faithfulness in Open Domain Table-to-text Generation from an Entity-centric View | https://ojs.aaai.org/index.php/AAAI/article/view/17583 | https://ojs.aaai.org/index.php/AAAI/article/download/17583/17390 | [
"Tianyu Liu",
"Xin Zheng",
"Baobao Chang",
"Zhifang Sui"
] | In open domain table-to-text generation, we notice the unfaithful generation usually contains hallucinated entities which can not be aligned to any input table record. We thus try to evaluate the generation faithfulness with two entity-centric metrics: table record coverage and the ratio of hallucinated entities in tex... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i15.17583 | 35 | 15 | 13415-13423 | official | 2102.08585 | title_snapshot |
10.1609/aaai.v35i15.17584 | Faster Depth-Adaptive Transformers | https://ojs.aaai.org/index.php/AAAI/article/view/17584 | https://ojs.aaai.org/index.php/AAAI/article/download/17584/17391 | [
"Yijin Liu",
"Fandong Meng",
"Jie Zhou",
"Yufeng Chen",
"Jinan Xu"
] | Depth-adaptive neural networks can dynamically adjust depths according to the hardness of input words, and thus improve efficiency. The main challenge is how to measure such hardness and decide the required depths (i.e., layers) to conduct. Previous works generally build a halting unit to decide whether the computation... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i15.17584 | 35 | 15 | 13424-13432 | official | 2004.13542 | title_snapshot |
10.1609/aaai.v35i15.17585 | A Graph Reasoning Network for Multi-turn Response Selection via Customized Pre-training | https://ojs.aaai.org/index.php/AAAI/article/view/17585 | https://ojs.aaai.org/index.php/AAAI/article/download/17585/17392 | [
"Yongkang Liu",
"Shi Feng",
"Daling Wang",
"Kaisong Song",
"Feiliang Ren",
"Yifei Zhang"
] | We investigate response selection for multi-turn conversation in retrieval-based chatbots. Existing studies pay more attention to the matching between utterances and responses by calculating the matching score based on learned features, leading to insufficient model reasoning ability. In this paper, we propose a graph-... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i15.17585 | 35 | 15 | 13433-13442 | official | 2012.11099 | title_snapshot |
10.1609/aaai.v35i15.17586 | Generating CCG Categories | https://ojs.aaai.org/index.php/AAAI/article/view/17586 | https://ojs.aaai.org/index.php/AAAI/article/download/17586/17393 | [
"Yufang Liu",
"Tao Ji",
"Yuanbin Wu",
"Man Lan"
] | Previous CCG supertaggers usually predict categories using multi-class classification. Despite their simplicity, internal structures of categories are usually ignored. The rich semantics inside these structures may help us to better handle relations among categories and bring more robustness into existing supertaggers.... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i15.17586 | 35 | 15 | 13443-13451 | official | 2103.08139 | title_snapshot |
10.1609/aaai.v35i15.17587 | CrossNER: Evaluating Cross-Domain Named Entity Recognition | https://ojs.aaai.org/index.php/AAAI/article/view/17587 | https://ojs.aaai.org/index.php/AAAI/article/download/17587/17394 | [
"Zihan Liu",
"Yan Xu",
"Tiezheng Yu",
"Wenliang Dai",
"Ziwei Ji",
"Samuel Cahyawijaya",
"Andrea Madotto",
"Pascale Fung"
] | Cross-domain named entity recognition (NER) models are able to cope with the scarcity issue of NER samples in target domains. However, most of the existing NER benchmarks lack domain-specialized entity types or do not focus on a certain domain, leading to a less effective cross-domain evaluation. To address these obsta... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i15.17587 | 35 | 15 | 13452-13460 | official | 2012.04373 | title_snapshot |
10.1609/aaai.v35i15.17588 | On the Importance of Word Order Information in Cross-lingual Sequence Labeling | https://ojs.aaai.org/index.php/AAAI/article/view/17588 | https://ojs.aaai.org/index.php/AAAI/article/download/17588/17395 | [
"Zihan Liu",
"Genta I Winata",
"Samuel Cahyawijaya",
"Andrea Madotto",
"Zhaojiang Lin",
"Pascale Fung"
] | Cross-lingual models trained on source language tasks possess the capability to directly transfer to target languages. However, since word order variances generally exist in different languages, cross-lingual models that overfit into the word order of the source language could have sub-optimal performance in target lan... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i15.17588 | 35 | 15 | 13461-13469 | official | 2001.11164 | title_snapshot |
10.1609/aaai.v35i15.17589 | SCRUPLES: A Corpus of Community Ethical Judgments on 32,000 Real-Life Anecdotes | https://ojs.aaai.org/index.php/AAAI/article/view/17589 | https://ojs.aaai.org/index.php/AAAI/article/download/17589/17396 | [
"Nicholas Lourie",
"Ronan Le Bras",
"Yejin Choi"
] | As AI systems become an increasing part of people's everyday lives, it becomes ever more important that they understand people's ethical norms. Motivated by descriptive ethics, a field of study that focuses on people's descriptive judgments rather than theoretical prescriptions on morality, we investigate a novel, data... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i15.17589 | 35 | 15 | 13470-13479 | official | 2008.09094 | title_snapshot |
10.1609/aaai.v35i15.17590 | UNICORN on RAINBOW: A Universal Commonsense Reasoning Model on a New Multitask Benchmark | https://ojs.aaai.org/index.php/AAAI/article/view/17590 | https://ojs.aaai.org/index.php/AAAI/article/download/17590/17397 | [
"Nicholas Lourie",
"Ronan Le Bras",
"Chandra Bhagavatula",
"Yejin Choi"
] | Commonsense AI has long been seen as a near impossible goal---until recently. Now, research interest has sharply increased with an influx of new benchmarks and models. We propose two new ways to evaluate commonsense models, emphasizing their generality on new tasks and building on diverse, recently introduced benchmark... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i15.17590 | 35 | 15 | 13480-13488 | official | 2103.13009 | title_snapshot |
10.1609/aaai.v35i15.17591 | Span-Based Event Coreference Resolution | https://ojs.aaai.org/index.php/AAAI/article/view/17591 | https://ojs.aaai.org/index.php/AAAI/article/download/17591/17398 | [
"Jing Lu",
"Vincent Ng"
] | Motivated by the recent successful application of span-based models to entity-based information extraction tasks, we investigate span-based models for event coreference resolution, focusing on determining (1) whether the successes of span-based models of entity coreference can be extended to event coreference; (2) whet... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i15.17591 | 35 | 15 | 13489-13497 | official | null | null |
10.1609/aaai.v35i15.17593 | Knowledge-driven Data Construction for Zero-shot Evaluation in Commonsense Question Answering | https://ojs.aaai.org/index.php/AAAI/article/view/17593 | https://ojs.aaai.org/index.php/AAAI/article/download/17593/17400 | [
"Kaixin Ma",
"Filip Ilievski",
"Jonathan Francis",
"Yonatan Bisk",
"Eric Nyberg",
"Alessandro Oltramari"
] | Recent developments in pre-trained neural language modeling have led to leaps in accuracy on common-sense question-answering benchmarks. However, there is increasing concern that models overfit to specific tasks, without learning to utilize external knowledge or perform general semantic reasoning. In contrast, zero-sho... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i15.17593 | 35 | 15 | 13507-13515 | official | 2011.03863 | title_snapshot |
10.1609/aaai.v35i15.17594 | Generate Your Counterfactuals: Towards Controlled Counterfactual Generation for Text | https://ojs.aaai.org/index.php/AAAI/article/view/17594 | https://ojs.aaai.org/index.php/AAAI/article/download/17594/17401 | [
"Nishtha Madaan",
"Inkit Padhi",
"Naveen Panwar",
"Diptikalyan Saha"
] | Machine Learning has seen tremendous growth recently, which has led to a larger adaptation of ML systems for educational assessments, credit risk, healthcare, employment, criminal justice, to name a few. The trustworthiness of ML and NLP systems is a crucial aspect and requires a guarantee that the decisions they make ... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i15.17594 | 35 | 15 | 13516-13524 | official | 2012.04698 | title_snapshot |
10.1609/aaai.v35i15.17595 | Generating Natural Language Attacks in a Hard Label Black Box Setting | https://ojs.aaai.org/index.php/AAAI/article/view/17595 | https://ojs.aaai.org/index.php/AAAI/article/download/17595/17402 | [
"Rishabh Maheshwary",
"Saket Maheshwary",
"Vikram Pudi"
] | We study an important and challenging task of attacking natural language processing models in a hard label black box setting. We propose a decision-based attack strategy that crafts high quality adversarial examples on text classification and entailment tasks. Our proposed attack strategy leverages population-based opt... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i15.17595 | 35 | 15 | 13525-13533 | official | 2012.14956 | title_snapshot |
10.1609/aaai.v35i15.17596 | Bridging Towers of Multi-task Learning with a Gating Mechanism for Aspect-based Sentiment Analysis and Sequential Metaphor Identification | https://ojs.aaai.org/index.php/AAAI/article/view/17596 | https://ojs.aaai.org/index.php/AAAI/article/download/17596/17403 | [
"Rui Mao",
"Xiao Li"
] | Multi-task learning (MTL) has been widely applied in Natural Language Processing. A major task and its associated auxiliary tasks share the same encoder; hence, an MTL encoder can learn the sharing abstract information between the major and auxiliary tasks. Task-specific towers are then employed upon the sharing encode... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i15.17596 | 35 | 15 | 13534-13542 | official | null | null |
10.1609/aaai.v35i15.17597 | A Joint Training Dual-MRC Framework for Aspect Based Sentiment Analysis | https://ojs.aaai.org/index.php/AAAI/article/view/17597 | https://ojs.aaai.org/index.php/AAAI/article/download/17597/17404 | [
"Yue Mao",
"Yi Shen",
"Chao Yu",
"Longjun Cai"
] | Aspect based sentiment analysis (ABSA) involves three fundamental subtasks: aspect term extraction, opinion term extraction, and aspect-level sentiment classification. Early works only focused on solving one of these subtasks individually. Some recent work focused on solving a combination of two subtasks, e.g., extract... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i15.17597 | 35 | 15 | 13543-13551 | official | 2101.00816 | title_snapshot |
10.1609/aaai.v35i15.17598 | Variational Inference for Learning Representations of Natural Language Edits | https://ojs.aaai.org/index.php/AAAI/article/view/17598 | https://ojs.aaai.org/index.php/AAAI/article/download/17598/17405 | [
"Edison Marrese-Taylor",
"Machel Reid",
"Yutaka Matsuo"
] | Document editing has become a pervasive component of production of information, with version control systems enabling edits to be efficiently stored and applied. In light of this, the task of learning distributed representations of edits has been recently proposed. With this in mind, we propose a novel approach that em... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i15.17598 | 35 | 15 | 13552-13560 | official | 2004.09143 | title_snapshot |
10.1609/aaai.v35i15.17599 | How Robust are Model Rankings : A Leaderboard Customization Approach for Equitable Evaluation | https://ojs.aaai.org/index.php/AAAI/article/view/17599 | https://ojs.aaai.org/index.php/AAAI/article/download/17599/17406 | [
"Swaroop Mishra",
"Anjana Arunkumar"
] | Models that top leaderboards often perform unsatisfactorily when deployed in real world applications; this has necessitated rigorous and expensive pre-deployment model testing. A hitherto unexplored facet of model performance is: Are our leaderboards doing equitable evaluation? In this paper, we introduce a task-agnost... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i15.17599 | 35 | 15 | 13561-13569 | official | 2106.05532 | title_snapshot |
10.1609/aaai.v35i15.17592 | LET: Linguistic Knowledge Enhanced Graph Transformer for Chinese Short Text Matching | https://ojs.aaai.org/index.php/AAAI/article/view/17592 | https://ojs.aaai.org/index.php/AAAI/article/download/17592/17399 | [
"Boer Lyu",
"Lu Chen",
"Su Zhu",
"Kai Yu"
] | Chinese short text matching is a fundamental task in natural language processing. Existing approaches usually take Chinese characters or words as input tokens. They have two limitations: 1) Some Chinese words are polysemous, and semantic information is not fully utilized. 2) Some models suffer potential issues caused b... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i15.17592 | 35 | 15 | 13498-13506 | official | 2102.12671 | title_snapshot |
10.1609/aaai.v35i15.17572 | An Efficient Transformer Decoder with Compressed Sub-layers | https://ojs.aaai.org/index.php/AAAI/article/view/17572 | https://ojs.aaai.org/index.php/AAAI/article/download/17572/17379 | [
"Yanyang Li",
"Ye Lin",
"Tong Xiao",
"Jingbo Zhu"
] | The large attention-based encoder-decoder network (Transformer) has become prevailing recently due to its effectiveness. But the high computation complexity of its decoder raises the inefficiency issue. By examining the mathematic formulation of the decoder, we show that under some mild conditions, the architecture cou... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i15.17572 | 35 | 15 | 13315-13323 | official | 2101.00542 | title_snapshot |
10.1609/aaai.v35i15.17571 | Interpretable NLG for Task-oriented Dialogue Systems with Heterogeneous Rendering Machines | https://ojs.aaai.org/index.php/AAAI/article/view/17571 | https://ojs.aaai.org/index.php/AAAI/article/download/17571/17378 | [
"Yangming Li",
"Kaisheng Yao"
] | End-to-end neural networks have achieved promising performances in natural language generation (NLG). However, they are treated as black boxes and lack interpretability. To address this problem, we propose a novel framework, heterogeneous rendering machines (HRM), that interprets how neural generators render an input d... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i15.17571 | 35 | 15 | 13306-13314 | official | 2012.14645 | title_snapshot |
10.1609/aaai.v35i15.17570 | TSQA: Tabular Scenario Based Question Answering | https://ojs.aaai.org/index.php/AAAI/article/view/17570 | https://ojs.aaai.org/index.php/AAAI/article/download/17570/17377 | [
"Xiao Li",
"Yawei Sun",
"Gong Cheng"
] | Scenario-based question answering (SQA) has attracted an increasing research interest. Compared with the well-studied machine reading comprehension (MRC), SQA is a more challenging task: a scenario may contain not only a textual passage to read but also structured data like tables, i.e., tabular scenario based question... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i15.17570 | 35 | 15 | 13297-13305 | official | 2101.11429 | title_snapshot |
10.1609/aaai.v35i15.17569 | Merging Statistical Feature via Adaptive Gate for Improved Text Classification | https://ojs.aaai.org/index.php/AAAI/article/view/17569 | https://ojs.aaai.org/index.php/AAAI/article/download/17569/17376 | [
"Xianming Li",
"Zongxi Li",
"Haoran Xie",
"Qing Li"
] | Currently, text classification studies mainly focus on training classifiers by using textual input only, or enhancing semantic features by introducing external knowledge (e.g., hand-craft lexicons and domain knowledge). In contrast, some intrinsic statistical features of the corpus, like word frequency and distribution... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i15.17569 | 35 | 15 | 13288-13296 | official | null | null |
10.1609/aaai.v35i15.17568 | HopRetriever: Retrieve Hops over Wikipedia to Answer Complex Questions | https://ojs.aaai.org/index.php/AAAI/article/view/17568 | https://ojs.aaai.org/index.php/AAAI/article/download/17568/17375 | [
"Shaobo Li",
"Xiaoguang Li",
"Lifeng Shang",
"Xin Jiang",
"Qun Liu",
"Chengjie Sun",
"Zhenzhou Ji",
"Bingquan Liu"
] | Collecting supporting evidence from large corpora of text (e.g., Wikipedia) is of great challenge for open-domain Question Answering (QA). Especially, for multi-hop open-domain QA, scattered evidence pieces are required to be gathered together to support the answer extraction. In this paper, we propose a new retrieval ... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i15.17568 | 35 | 15 | 13279-13287 | official | 2012.15534 | title_snapshot |
10.1609/aaai.v35i15.17567 | Quantum-inspired Neural Network for Conversational Emotion Recognition | https://ojs.aaai.org/index.php/AAAI/article/view/17567 | https://ojs.aaai.org/index.php/AAAI/article/download/17567/17374 | [
"Qiuchi Li",
"Dimitris Gkoumas",
"Alessandro Sordoni",
"Jian-Yun Nie",
"Massimo Melucci"
] | We provide a novel perspective on conversational emotion recognition by drawing an analogy between the task and a complete span of quantum measurement. We characterize different steps of quantum measurement in the process of recognizing speakers' emotions in conversation, and stitch them up with a quantum-like neural n... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i15.17567 | 35 | 15 | 13270-13278 | official | null | null |
10.1609/aaai.v35i15.17566 | ACT: an Attentive Convolutional Transformer for Efficient Text Classification | https://ojs.aaai.org/index.php/AAAI/article/view/17566 | https://ojs.aaai.org/index.php/AAAI/article/download/17566/17373 | [
"Pengfei Li",
"Peixiang Zhong",
"Kezhi Mao",
"Dongzhe Wang",
"Xuefeng Yang",
"Yunfeng Liu",
"Jianxiong Yin",
"Simon See"
] | Recently, Transformer has been demonstrating promising performance in many NLP tasks and showing a trend of replacing Recurrent Neural Network (RNN). Meanwhile, less attention is drawn to Convolutional Neural Network (CNN) due to its weak ability in capturing sequential and long-distance dependencies, although it has e... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i15.17566 | 35 | 15 | 13261-13269 | official | null | null |
10.1609/aaai.v35i15.17565 | The Style-Content Duality of Attractiveness: Learning to Write Eye-Catching Headlines via Disentanglement | https://ojs.aaai.org/index.php/AAAI/article/view/17565 | https://ojs.aaai.org/index.php/AAAI/article/download/17565/17372 | [
"Mingzhe Li",
"Xiuying Chen",
"Min Yang",
"Shen Gao",
"Dongyan Zhao",
"Rui Yan"
] | Eye-catching headlines function as the first device to trigger more clicks, bringing reciprocal effect between producers and viewers. Producers can obtain more traffic and profits, and readers can have access to outstanding articles. When generating attractive headlines, it is important to not only capture the attracti... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i15.17565 | 35 | 15 | 13252-13260 | official | 2012.07419 | title_snapshot |
10.1609/aaai.v35i15.17564 | Towards Topic-Aware Slide Generation For Academic Papers With Unsupervised Mutual Learning | https://ojs.aaai.org/index.php/AAAI/article/view/17564 | https://ojs.aaai.org/index.php/AAAI/article/download/17564/17371 | [
"Da-Wei Li",
"Danqing Huang",
"Tingting Ma",
"Chin-Yew Lin"
] | Slides are commonly used to present information and tell stories. In academic and research communities, slides are typically used to summarize findings in accepted papers for presentation in meetings and conferences. These slides for academic papers usually contain common and essential topics such as major contribution... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i15.17564 | 35 | 15 | 13243-13251 | official | null | null |
10.1609/aaai.v35i15.17563 | Multi-view Inference for Relation Extraction with Uncertain Knowledge | https://ojs.aaai.org/index.php/AAAI/article/view/17563 | https://ojs.aaai.org/index.php/AAAI/article/download/17563/17370 | [
"Bo Li",
"Wei Ye",
"Canming Huang",
"Shikun Zhang"
] | Knowledge graphs (KGs) are widely used to facilitate relation extraction (RE) tasks. While most previous RE methods focus on leveraging deterministic KGs, uncertain KGs, which assign a confidence score for each relation instance, can provide prior probability distributions of relational facts as valuable external knowl... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i15.17563 | 35 | 15 | 13234-13242 | official | 2104.13579 | title_snapshot |
10.1609/aaai.v35i15.17581 | How to Train Your Agent to Read and Write | https://ojs.aaai.org/index.php/AAAI/article/view/17581 | https://ojs.aaai.org/index.php/AAAI/article/download/17581/17388 | [
"Li Liu",
"Mengge He",
"Guanghui Xu",
"Mingkui Tan",
"Qi Wu"
] | Reading and writing research papers is one of the most privileged abilities that a qualified researcher should master. However, it is difficult for new researchers (e.g., students) to fully grasp this ability. It would be fascinating if we could train an intelligent agent to help people read and summarize papers, and p... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i15.17581 | 35 | 15 | 13397-13405 | official | 2101.00916 | title_snapshot |
10.1609/aaai.v35i15.17573 | An Unsupervised Sampling Approach for Image-Sentence Matching Using Document-level Structural Information | https://ojs.aaai.org/index.php/AAAI/article/view/17573 | https://ojs.aaai.org/index.php/AAAI/article/download/17573/17380 | [
"Zejun Li",
"Zhongyu Wei",
"Zhihao Fan",
"Haijun Shan",
"Xuanjing Huang"
] | In this paper, we focus on the problem of unsupervised image-sentence matching. Existing research explores to utilize document-level structural information to sample positive and negative instances for model training. Although the approach achieves positive results, it introduces a sampling bias and fails to distinguis... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i15.17573 | 35 | 15 | 13324-13332 | official | 2104.02605 | title_snapshot |
10.1609/aaai.v35i15.17574 | Finding Sparse Structures for Domain Specific Neural Machine Translation | https://ojs.aaai.org/index.php/AAAI/article/view/17574 | https://ojs.aaai.org/index.php/AAAI/article/download/17574/17381 | [
"Jianze Liang",
"Chengqi Zhao",
"Mingxuan Wang",
"Xipeng Qiu",
"Lei Li"
] | Neural machine translation often adopts the fine-tuning approach to adapt to specific domains. However, nonrestricted fine-tuning can easily degrade on the general domain and over-fit to the target domain. To mitigate the issue, we propose Prune-Tune, a novel domain adaptation method via gradual pruning. It learns tiny... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i15.17574 | 35 | 15 | 13333-13342 | official | 2012.10586 | title_snapshot |
10.1609/aaai.v35i15.17575 | Infusing Multi-Source Knowledge with Heterogeneous Graph Neural Network for Emotional Conversation Generation | https://ojs.aaai.org/index.php/AAAI/article/view/17575 | https://ojs.aaai.org/index.php/AAAI/article/download/17575/17382 | [
"Yunlong Liang",
"Fandong Meng",
"Ying Zhang",
"Yufeng Chen",
"Jinan Xu",
"Jie Zhou"
] | The success of emotional conversation systems depends on sufficient perception and appropriate expression of emotions. In a real-world conversation, we firstly instinctively perceive emotions from multi-source information, including the emotion flow of dialogue history, facial expressions, and personalities of speakers... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i15.17575 | 35 | 15 | 13343-13352 | official | null | null |
10.1609/aaai.v35i15.17576 | Hierarchical Coherence Modeling for Document Quality Assessment | https://ojs.aaai.org/index.php/AAAI/article/view/17576 | https://ojs.aaai.org/index.php/AAAI/article/download/17576/17383 | [
"Dongliang Liao",
"Jin Xu",
"Gongfu Li",
"Yiru Wang"
] | Text coherence plays a key role in document quality assessment. Most existing text coherence methods only focus on similarity of adjacent sentences. However, local coherence exists in sentences with broader contexts and diverse rhetoric relations, rather than just adjacent sentences similarity. Besides, the highlevel t... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i15.17576 | 35 | 15 | 13353-13361 | official | null | null |
10.1609/aaai.v35i15.17577 | Graph-Evolving Meta-Learning for Low-Resource Medical Dialogue Generation | https://ojs.aaai.org/index.php/AAAI/article/view/17577 | https://ojs.aaai.org/index.php/AAAI/article/download/17577/17384 | [
"Shuai Lin",
"Pan Zhou",
"Xiaodan Liang",
"Jianheng Tang",
"Ruihui Zhao",
"Ziliang Chen",
"Liang Lin"
] | Human doctors with well-structured medical knowledge can diagnose a disease merely via a few conversations with patients about symptoms. In contrast, existing knowledge-grounded dialogue systems often require a large number of dialogue instances to learn as they fail to capture the correlations between different diseas... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i15.17577 | 35 | 15 | 13362-13370 | official | 2012.11988 | title_snapshot |
10.1609/aaai.v35i15.17578 | Neural Sentence Simplification with Semantic Dependency Information | https://ojs.aaai.org/index.php/AAAI/article/view/17578 | https://ojs.aaai.org/index.php/AAAI/article/download/17578/17385 | [
"Zhe Lin",
"Xiaojun Wan"
] | Most previous works on neural sentence simplification exploit seq2seq model to rewrite a sentence without explicitly considering the semantic information of the sentence. This may lead to the semantic deviation of the simplified sentence. In this paper, we leverage semantic dependency graph to aid neural sentence simpl... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i15.17578 | 35 | 15 | 13371-13379 | official | null | null |
10.1609/aaai.v35i15.17579 | Converse, Focus and Guess - Towards Multi-Document Driven Dialogue | https://ojs.aaai.org/index.php/AAAI/article/view/17579 | https://ojs.aaai.org/index.php/AAAI/article/download/17579/17386 | [
"Han Liu",
"Caixia Yuan",
"Xiaojie Wang",
"Yushu Yang",
"Huixing Jiang",
"Zhongyuan Wang"
] | We propose a novel task, Multi-Document Driven Dialogue (MD3), in which an agent can guess the target document that the user is interested in by leading a dialogue. To benchmark progress, we introduce a new dataset of GuessMovie, which contains 16,881 documents, each describing a movie, and associated 13,434 dialogues.... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i15.17579 | 35 | 15 | 13380-13387 | official | 2102.02435 | title_snapshot |
10.1609/aaai.v35i15.17580 | Natural Language Inference in Context - Investigating Contextual Reasoning over Long Texts | https://ojs.aaai.org/index.php/AAAI/article/view/17580 | https://ojs.aaai.org/index.php/AAAI/article/download/17580/17387 | [
"Hanmeng Liu",
"Leyang Cui",
"Jian Liu",
"Yue Zhang"
] | Natural language inference (NLI) is a fundamental NLP task, investigating the entailment relationship between two texts. Popular NLI datasets present the task at sentence-level. While adequate for testing semantic representations, they fall short for testing contextual reasoning over long texts, which is a natural part... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i15.17580 | 35 | 15 | 13388-13396 | official | 2011.04864 | title_snapshot |
10.1609/aaai.v35i15.17582 | Filling the Gap of Utterance-aware and Speaker-aware Representation for Multi-turn Dialogue | https://ojs.aaai.org/index.php/AAAI/article/view/17582 | https://ojs.aaai.org/index.php/AAAI/article/download/17582/17389 | [
"Longxiang Liu",
"Zhuosheng Zhang",
"Hai Zhao",
"Xi Zhou",
"Xiang Zhou"
] | A multi-turn dialogue is composed of multiple utterances from two or more different speaker roles. Thus utterance- and speaker-aware clues are supposed to be well captured in models. However, in the existing retrieval-based multi-turn dialogue modeling, the pre-trained language models (PrLMs) as encoder represent the d... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i15.17582 | 35 | 15 | 13406-13414 | official | 2009.06504 | title_snapshot |
10.1609/aaai.v35i15.17560 | Have We Solved The Hard Problem? It’s Not Easy! Contextual Lexical Contrast as a Means to Probe Neural Coherence | https://ojs.aaai.org/index.php/AAAI/article/view/17560 | https://ojs.aaai.org/index.php/AAAI/article/download/17560/17367 | [
"Wenqiang Lei",
"Yisong Miao",
"Runpeng Xie",
"Bonnie Webber",
"Meichun Liu",
"Tat-Seng Chua",
"Nancy F. Chen"
] | Lexical cohesion is a fundamental mechanism for text which requires a pair of words to be interpreted as a certain type of lexical relation (e.g., similarity) to understand a coherent context; we refer to such relations as the contextual lexical relation. However, work on lexical cohesion has not modeled context compre... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i15.17560 | 35 | 15 | 13208-13216 | official | null | null |
10.1609/aaai.v35i15.17562 | Improving the Efficiency and Effectiveness for BERT-based Entity Resolution | https://ojs.aaai.org/index.php/AAAI/article/view/17562 | https://ojs.aaai.org/index.php/AAAI/article/download/17562/17369 | [
"Bing Li",
"Yukai Miao",
"Yaoshu Wang",
"Yifang Sun",
"Wei Wang"
] | BERT has set a new state-of-the-art performance on entity resolution (ER) task, largely owed to fine-tuning pre-trained language models and the deep pair-wise interaction. Albeit being remarkably effective, it comes with a steep increase in computational cost, as the deep-interaction requires to exhaustively compute ev... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i15.17562 | 35 | 15 | 13226-13233 | official | null | null |
10.1609/aaai.v35i15.17561 | Learning Light-Weight Translation Models from Deep Transformer | https://ojs.aaai.org/index.php/AAAI/article/view/17561 | https://ojs.aaai.org/index.php/AAAI/article/download/17561/17368 | [
"Bei Li",
"Ziyang Wang",
"Hui Liu",
"Quan Du",
"Tong Xiao",
"Chunliang Zhang",
"Jingbo Zhu"
] | Recently, deep models have shown tremendous improvements in neural machine translation (NMT). However, systems of this kind are computationally expensive and memory intensive. In this paper, we take a natural step towards learning strong but light-weight NMT systems. We proposed a novel group-permutation based knowledg... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i15.17561 | 35 | 15 | 13217-13225 | official | 2012.13866 | title_snapshot |
10.1609/aaai.v35i16.17724 | Unsupervised Summarization for Chat Logs with Topic-Oriented Ranking and Context-Aware Auto-Encoders | https://ojs.aaai.org/index.php/AAAI/article/view/17724 | https://ojs.aaai.org/index.php/AAAI/article/download/17724/17531 | [
"Yicheng Zou",
"Jun Lin",
"Lujun Zhao",
"Yangyang Kang",
"Zhuoren Jiang",
"Changlong Sun",
"Qi Zhang",
"Xuanjing Huang",
"Xiaozhong Liu"
] | Automatic chat summarization can help people quickly grasp important information from numerous chat messages. Unlike conventional documents, chat logs usually have fragmented and evolving topics. In addition, these logs contain a quantity of elliptical and interrogative sentences, which make the chat summarization high... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i16.17724 | 35 | 16 | 14674-14682 | official | 2012.07300 | title_snapshot |
10.1609/aaai.v35i16.17723 | Topic-Oriented Spoken Dialogue Summarization for Customer Service with Saliency-Aware Topic Modeling | https://ojs.aaai.org/index.php/AAAI/article/view/17723 | https://ojs.aaai.org/index.php/AAAI/article/download/17723/17530 | [
"Yicheng Zou",
"Lujun Zhao",
"Yangyang Kang",
"Jun Lin",
"Minlong Peng",
"Zhuoren Jiang",
"Changlong Sun",
"Qi Zhang",
"Xuanjing Huang",
"Xiaozhong Liu"
] | In a customer service system, dialogue summarization can boost service efficiency by automatically creating summaries for long spoken dialogues in which customers and agents try to address issues about specific topics. In this work, we focus on topic-oriented dialogue summarization, which generates highly abstractive s... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i16.17723 | 35 | 16 | 14665-14673 | official | 2012.07311 | title_snapshot |
10.1609/aaai.v35i16.17704 | News Content Completion with Location-Aware Image Selection | https://ojs.aaai.org/index.php/AAAI/article/view/17704 | https://ojs.aaai.org/index.php/AAAI/article/download/17704/17511 | [
"Zhengkun Zhang",
"Jun Wang",
"Adam Jatowt",
"Zhe Sun",
"Shao-Ping Lu",
"Zhenglu Yang"
] | News, as one of the fundamental social media types, typically contains both texts and images. Image selection, which involves choosing appropriate images according to some specified contexts, is crucial for formulating good news. However, it presents two challenges: where to place images and which images to use. The di... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i16.17704 | 35 | 16 | 14498-14505 | official | null | null |
10.1609/aaai.v35i16.17703 | Unsupervised Abstractive Dialogue Summarization for Tete-a-Tetes | https://ojs.aaai.org/index.php/AAAI/article/view/17703 | https://ojs.aaai.org/index.php/AAAI/article/download/17703/17510 | [
"Xinyuan Zhang",
"Ruiyi Zhang",
"Manzil Zaheer",
"Amr Ahmed"
] | High-quality dialogue-summary paired data is expensive to produce and domain-sensitive, making abstractive dialogue summarization a challenging task. In this work, we propose the first unsupervised abstractive dialogue summarization model for tete-a-tetes (SuTaT). Unlike standard text summarization, a dialogue summariz... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i16.17703 | 35 | 16 | 14489-14497 | official | 2009.06851 | title_snapshot |
10.1609/aaai.v35i16.17705 | Retrospective Reader for Machine Reading Comprehension | https://ojs.aaai.org/index.php/AAAI/article/view/17705 | https://ojs.aaai.org/index.php/AAAI/article/download/17705/17512 | [
"Zhuosheng Zhang",
"Junjie Yang",
"Hai Zhao"
] | Machine reading comprehension (MRC) is an AI challenge that requires machines to determine the correct answers to questions based on a given passage. MRC systems must not only answer questions when necessary but also tactfully abstain from answering when no answer is available according to the given passage. When unans... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i16.17705 | 35 | 16 | 14506-14514 | official | 2001.09694 | title_snapshot |
10.1609/aaai.v35i16.17706 | Dynamic Modeling Cross- and Self-Lattice Attention Network for Chinese NER | https://ojs.aaai.org/index.php/AAAI/article/view/17706 | https://ojs.aaai.org/index.php/AAAI/article/download/17706/17513 | [
"Shan Zhao",
"Minghao Hu",
"Zhiping Cai",
"Haiwen Chen",
"Fang Liu"
] | Word-character lattice models have been proved to be effective for Chinese named entity recognition (NER), in which word boundary information is fused into character sequences for enhancing character representations. However, prior approaches have only used simple methods such as feature concatenation or position encod... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i16.17706 | 35 | 16 | 14515-14523 | official | null | null |
10.1609/aaai.v35i16.17707 | A Unified Multi-Task Learning Framework for Joint Extraction of Entities and Relations | https://ojs.aaai.org/index.php/AAAI/article/view/17707 | https://ojs.aaai.org/index.php/AAAI/article/download/17707/17514 | [
"Tianyang Zhao",
"Zhao Yan",
"Yunbo Cao",
"Zhoujun Li"
] | Joint extraction of entities and relations focuses on detecting entity pairs and their relations simultaneously with a unified model. Based on the extraction order, previous works mainly solve this task through relation-last, relation-first and relation-middle manner. However, these methods still suffer from the templa... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i16.17707 | 35 | 16 | 14524-14531 | official | null | null |
10.1609/aaai.v35i16.17708 | LIREx: Augmenting Language Inference with Relevant Explanations | https://ojs.aaai.org/index.php/AAAI/article/view/17708 | https://ojs.aaai.org/index.php/AAAI/article/download/17708/17515 | [
"Xinyan Zhao",
"V.G.Vinod Vydiswaran"
] | Natural language explanations (NLEs) are a special form of data annotation in which annotators identify rationales (most significant text tokens) when assigning labels to data instances, and write out explanations for the labels in natural language based on the rationales. NLEs have been shown to capture human reasonin... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i16.17708 | 35 | 16 | 14532-14539 | official | 2012.09157 | title_judge |
10.1609/aaai.v35i16.17709 | Automatic Curriculum Learning With Over-repetition Penalty for Dialogue Policy Learning | https://ojs.aaai.org/index.php/AAAI/article/view/17709 | https://ojs.aaai.org/index.php/AAAI/article/download/17709/17516 | [
"Yangyang Zhao",
"Zhenyu Wang",
"Zhenhua Huang"
] | Dialogue policy learning based on reinforcement learning is difficult to be applied to real users to train dialogue agents from scratch because of the high cost. User simulators, which choose random user goals for the dialogue agent to train on, have been considered as an affordable substitute for real users. However, ... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i16.17709 | 35 | 16 | 14540-14548 | official | 2012.14072 | title_snapshot |
10.1609/aaai.v35i16.17710 | Interactive Speech and Noise Modeling for Speech Enhancement | https://ojs.aaai.org/index.php/AAAI/article/view/17710 | https://ojs.aaai.org/index.php/AAAI/article/download/17710/17517 | [
"Chengyu Zheng",
"Xiulian Peng",
"Yuan Zhang",
"Sriram Srinivasan",
"Yan Lu"
] | Speech enhancement is challenging because of the diversity of background noise types. Most of the existing methods are focused on modelling the speech rather than the noise. In this paper, we propose a novel idea to model speech and noise simultaneously in a two-branch convolutional neural network, namely SN-Net. In SN... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i16.17710 | 35 | 16 | 14549-14557 | official | 2012.09408 | title_snapshot |
10.1609/aaai.v35i16.17711 | Stylized Dialogue Response Generation Using Stylized Unpaired Texts | https://ojs.aaai.org/index.php/AAAI/article/view/17711 | https://ojs.aaai.org/index.php/AAAI/article/download/17711/17518 | [
"Yinhe Zheng",
"Zikai Chen",
"Rongsheng Zhang",
"Shilei Huang",
"Xiaoxi Mao",
"Minlie Huang"
] | Generating stylized responses is essential to build intelligent and engaging dialogue systems. However, this task is far from well-explored due to the difficulties of rendering a particular style in coherent responses, especially when the target style is embedded only in unpaired texts that cannot be directly used to t... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i16.17711 | 35 | 16 | 14558-14567 | official | 2009.12719 | title_snapshot |
10.1609/aaai.v35i16.17712 | Keyword-Guided Neural Conversational Model | https://ojs.aaai.org/index.php/AAAI/article/view/17712 | https://ojs.aaai.org/index.php/AAAI/article/download/17712/17519 | [
"Peixiang Zhong",
"Yong Liu",
"Hao Wang",
"Chunyan Miao"
] | We study the problem of imposing conversational goals/keywords on open-domain conversational agents, where the agent is required to lead the conversation to a target keyword smoothly and fast. Solving this problem enables the application of conversational agents in many real-world scenarios, e.g., recommendation and ps... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i16.17712 | 35 | 16 | 14568-14576 | official | 2012.08383 | title_snapshot |
10.1609/aaai.v35i16.17714 | MTAAL: Multi-Task Adversarial Active Learning for Medical Named Entity Recognition and Normalization | https://ojs.aaai.org/index.php/AAAI/article/view/17714 | https://ojs.aaai.org/index.php/AAAI/article/download/17714/17521 | [
"Baohang Zhou",
"Xiangrui Cai",
"Ying Zhang",
"Wenya Guo",
"Xiaojie Yuan"
] | Automated medical named entity recognition and normalization are fundamental for constructing knowledge graphs and building QA systems. When it comes to medical text, the annotation demands a foundation of expertise and professionalism. Existing methods utilize active learning to reduce costs in corpus annotation, as w... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i16.17714 | 35 | 16 | 14586-14593 | official | null | null |
10.1609/aaai.v35i16.17722 | Neural Sentence Ordering Based on Constraint Graphs | https://ojs.aaai.org/index.php/AAAI/article/view/17722 | https://ojs.aaai.org/index.php/AAAI/article/download/17722/17529 | [
"Yutao Zhu",
"Kun Zhou",
"Jian-Yun Nie",
"Shengchao Liu",
"Zhicheng Dou"
] | Sentence ordering aims at arranging a list of sentences in the correct order. Based on the observation that sentence order at different distances may rely on different types of information, we devise a new approach based on multi-granular orders between sentences. These orders form multiple constraint graphs, which are... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i16.17722 | 35 | 16 | 14656-14664 | official | 2101.11178 | title_snapshot |
10.1609/aaai.v35i16.17713 | CARE: Commonsense-Aware Emotional Response Generation with Latent Concepts | https://ojs.aaai.org/index.php/AAAI/article/view/17713 | https://ojs.aaai.org/index.php/AAAI/article/download/17713/17520 | [
"Peixiang Zhong",
"Di Wang",
"Pengfei Li",
"Chen Zhang",
"Hao Wang",
"Chunyan Miao"
] | Rationality and emotion are two fundamental elements of humans. Endowing agents with rationality and emotion has been one of the major milestones in AI. However, in the field of conversational AI, most existing models only specialize in one aspect and neglect the other, which often leads to dull or unrelated responses.... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i16.17713 | 35 | 16 | 14577-14585 | official | 2012.08377 | title_snapshot |
10.1609/aaai.v35i16.17721 | Clinical Temporal Relation Extraction with Probabilistic Soft Logic Regularization and Global Inference | https://ojs.aaai.org/index.php/AAAI/article/view/17721 | https://ojs.aaai.org/index.php/AAAI/article/download/17721/17528 | [
"Yichao Zhou",
"Yu Yan",
"Rujun Han",
"J. Harry Caufield",
"Kai-Wei Chang",
"Yizhou Sun",
"Peipei Ping",
"Wei Wang"
] | There has been a steady need in the medical community to precisely extract the temporal relations between clinical events. In particular, temporal information can facilitate a variety of downstream applications such as case report retrieval and medical question answering. Existing methods either require expensive featu... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i16.17721 | 35 | 16 | 14647-14655 | official | 2012.08790 | title_snapshot |
10.1609/aaai.v35i16.17720 | What the Role is vs. What Plays the Role: Semi-Supervised Event Argument Extraction via Dual Question Answering | https://ojs.aaai.org/index.php/AAAI/article/view/17720 | https://ojs.aaai.org/index.php/AAAI/article/download/17720/17527 | [
"Yang Zhou",
"Yubo Chen",
"Jun Zhao",
"Yin Wu",
"Jiexin Xu",
"Jinlong Li"
] | Event argument extraction is an essential task in event extraction, and become particularly challenging in the case of low-resource scenarios. We solve the issues in existing studies under low-resource situations from two sides. From the perspective of the model, the existing methods always suffer from the concern of i... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i16.17720 | 35 | 16 | 14638-14646 | official | null | null |
10.1609/aaai.v35i16.17719 | An Adaptive Hybrid Framework for Cross-domain Aspect-based Sentiment Analysis | https://ojs.aaai.org/index.php/AAAI/article/view/17719 | https://ojs.aaai.org/index.php/AAAI/article/download/17719/17526 | [
"Yan Zhou",
"Fuqing Zhu",
"Pu Song",
"Jizhong Han",
"Tao Guo",
"Songlin Hu"
] | Cross-domain aspect-based sentiment analysis aims to utilize the useful knowledge in a source domain to extract aspect terms and predict their sentiment polarities in a target domain. Recently, methods based on adversarial training have been applied to this task and achieved promising results. In such methods, both the... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i16.17719 | 35 | 16 | 14630-14637 | official | null | null |
10.1609/aaai.v35i16.17718 | IsoBN: Fine-Tuning BERT with Isotropic Batch Normalization | https://ojs.aaai.org/index.php/AAAI/article/view/17718 | https://ojs.aaai.org/index.php/AAAI/article/download/17718/17525 | [
"Wenxuan Zhou",
"Bill Yuchen Lin",
"Xiang Ren"
] | Fine-tuning pre-trained language models (PTLMs), such as BERT and its better variant RoBERTa, has been a common practice for advancing performance in natural language understanding (NLU) tasks. Recent advance in representation learning shows that isotropic (i.e., unit-variance and uncorrelated) embeddings can significa... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i16.17718 | 35 | 16 | 14621-14629 | official | 2005.02178 | title_snapshot |
10.1609/aaai.v35i16.17717 | Document-Level Relation Extraction with Adaptive Thresholding and Localized Context Pooling | https://ojs.aaai.org/index.php/AAAI/article/view/17717 | https://ojs.aaai.org/index.php/AAAI/article/download/17717/17524 | [
"Wenxuan Zhou",
"Kevin Huang",
"Tengyu Ma",
"Jing Huang"
] | Document-level relation extraction (RE) poses new challenges compared to its sentence-level counterpart. One document commonly contains multiple entity pairs, and one entity pair occurs multiple times in the document associated with multiple possible relations. In this paper, we propose two novel techniques, adaptive t... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i16.17717 | 35 | 16 | 14612-14620 | official | 2010.11304 | title_snapshot |
10.1609/aaai.v35i16.17716 | EvaLDA: Efficient Evasion Attacks Towards Latent Dirichlet Allocation | https://ojs.aaai.org/index.php/AAAI/article/view/17716 | https://ojs.aaai.org/index.php/AAAI/article/download/17716/17523 | [
"Qi Zhou",
"Haipeng Chen",
"Yitao Zheng",
"Zhen Wang"
] | As one of the most powerful topic models, Latent Dirichlet Allocation (LDA) has been used in a vast range of tasks, including document understanding, information retrieval and peer-reviewer assignment. Despite its tremendous popularity, the security of LDA has rarely been studied. This poses severe risks to security-cr... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i16.17716 | 35 | 16 | 14602-14611 | official | 2012.04864 | title_snapshot |
10.1609/aaai.v35i16.17715 | A Neural Group-wise Sentiment Analysis Model with Data Sparsity Awareness | https://ojs.aaai.org/index.php/AAAI/article/view/17715 | https://ojs.aaai.org/index.php/AAAI/article/download/17715/17522 | [
"Deyu Zhou",
"Meng Zhang",
"Linhai Zhang",
"Yulan He"
] | Sentiment analysis on user-generated content has achieved notable progress by introducing user information to consider each individual’s preference and language usage. However, most existing approaches ignore the data sparsity problem, where the content of some users is limited and the model fails to capture discrimina... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i16.17715 | 35 | 16 | 14594-14601 | official | null | null |
10.1609/aaai.v35i16.17684 | UWSpeech: Speech to Speech Translation for Unwritten Languages | https://ojs.aaai.org/index.php/AAAI/article/view/17684 | https://ojs.aaai.org/index.php/AAAI/article/download/17684/17491 | [
"Chen Zhang",
"Xu Tan",
"Yi Ren",
"Tao Qin",
"Kejun Zhang",
"Tie-Yan Liu"
] | Existing speech to speech translation systems heavily rely on the text of target language: they usually translate source language either to target text and then synthesize target speech from text, or directly to target speech with target text for auxiliary training. However, those methods cannot be applied to unwritten... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i16.17684 | 35 | 16 | 14319-14327 | official | 2006.07926 | title_snapshot |
10.1609/aaai.v35i16.17685 | Building Interpretable Interaction Trees for Deep NLP Models | https://ojs.aaai.org/index.php/AAAI/article/view/17685 | https://ojs.aaai.org/index.php/AAAI/article/download/17685/17492 | [
"Die Zhang",
"Hao Zhang",
"Huilin Zhou",
"Xiaoyi Bao",
"Da Huo",
"Ruizhao Chen",
"Xu Cheng",
"Mengyue Wu",
"Quanshi Zhang"
] | This paper proposes a method to disentangle and quantify interactions among words that are encoded inside a DNN for natural language processing. We construct a tree to encode salient interactions extracted by the DNN. Six metrics are proposed to analyze properties of interactions between constituents in a sentence. The... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i16.17685 | 35 | 16 | 14328-14337 | official | 2007.04298 | title_snapshot |
10.1609/aaai.v35i16.17686 | Multi-modal Multi-label Emotion Recognition with Heterogeneous Hierarchical Message Passing | https://ojs.aaai.org/index.php/AAAI/article/view/17686 | https://ojs.aaai.org/index.php/AAAI/article/download/17686/17493 | [
"Dong Zhang",
"Xincheng Ju",
"Wei Zhang",
"Junhui Li",
"Shoushan Li",
"Qiaoming Zhu",
"Guodong Zhou"
] | As an important research issue in affective computing community, multi-modal emotion recognition has become a hot topic in the last few years. However, almost all existing studies perform multiple binary classification for each emotion with focus on complete time series data. In this paper, we focus on multi-modal emot... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i16.17686 | 35 | 16 | 14338-14346 | official | null | null |
10.1609/aaai.v35i16.17687 | RETRACTED: Multi-modal Graph Fusion for Named Entity Recognition with Targeted Visual Guidance | https://ojs.aaai.org/index.php/AAAI/article/view/17687 | https://ojs.aaai.org/index.php/AAAI/article/download/17687/17494 | [
"Dong Zhang",
"Suzhong Wei",
"Shoushan Li",
"Hanqian Wu",
"Qiaoming Zhu",
"Guodong Zhou"
] | Multi-modal named entity recognition (MNER) aims to discover named entities in free text and classify them into pre-defined types with images. However, dominant MNER models do not fully exploit fine-grained semantic correspondences between semantic units of different modalities, which have the potential to refine multi... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i16.17687 | 35 | 16 | 14347-14355 | official | null | null |
10.1609/aaai.v35i16.17688 | Accelerating Neural Machine Translation with Partial Word Embedding Compression | https://ojs.aaai.org/index.php/AAAI/article/view/17688 | https://ojs.aaai.org/index.php/AAAI/article/download/17688/17495 | [
"Fan Zhang",
"Mei Tu",
"Jinyao Yan"
] | Large model size and high computational complexity prevent the neural machine translation (NMT) models from being deployed to low resource devices (e.g. mobile phones). Due to the large vocabulary, a large storage memory is required for the word embedding matrix in NMT models, in the meantime, high latency is introduce... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i16.17688 | 35 | 16 | 14356-14364 | official | null | null |
10.1609/aaai.v35i16.17689 | Discovering New Intents with Deep Aligned Clustering | https://ojs.aaai.org/index.php/AAAI/article/view/17689 | https://ojs.aaai.org/index.php/AAAI/article/download/17689/17496 | [
"Hanlei Zhang",
"Hua Xu",
"Ting-En Lin",
"Rui Lyu"
] | Discovering new intents is a crucial task in dialogue systems. Most existing methods are limited in transferring the prior knowledge from known intents to new intents. These methods also have difficulties in providing high-quality supervised signals to learn clustering-friendly features for grouping unlabeled intents. ... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i16.17689 | 35 | 16 | 14365-14373 | official | 2012.08987 | title_snapshot |
10.1609/aaai.v35i16.17690 | Deep Open Intent Classification with Adaptive Decision Boundary | https://ojs.aaai.org/index.php/AAAI/article/view/17690 | https://ojs.aaai.org/index.php/AAAI/article/download/17690/17497 | [
"Hanlei Zhang",
"Hua Xu",
"Ting-En Lin"
] | Open intent classification is a challenging task in dialogue systems. On the one hand, it should ensure the quality of known intent identification. On the other hand, it needs to detect the open (unknown) intent without prior knowledge. Current models are limited in finding the appropriate decision boundary to balance ... | main | Speech and Natural Language Processing | 10.1609/aaai.v35i16.17690 | 35 | 16 | 14374-14382 | official | 2012.10209 | title_snapshot |
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