bluuebunny/crossref_metadata_2025 huggingface dataset:
Listen, Understand and Translate: Triple Supervision Decouples End-to-end Speech-to-text Translation
Authors: Dong Qianqian , Ye Rong , Wang Mingxuan , Zhou Hao , Xu Shuang , Xu Bo , Li Lei
Published: September 2022

An end-to-end speech-to-text translation (ST) takes audio in a source language and outputs the text in a target language. Existing methods are limited by the amount of parallel corpus. Can we build a system to fully utilize signals in a parallel ST corpus? We are inspired by human understanding system which is composed of auditory perception and cognitive processing. In this paper, we propose Listen-Understand-Translate, (LUT), a unified framework with triple supervision signals to decouple the end-to-end speech-to-text translation task. LUT is able to guide the acoustic encoder to extract as much information from the auditory input. In addition, LUT utilizes a pre-trained BERT model to enforce the upper encoder to produce as much semantic information as possible, without extra data. We perform experiments on a diverse set of speech translation benchmarks, including Librispeech English-French, IWSLT English-German and TED English-Chinese. Our results demonstrate LUT achieves the state-of-the-art performance, outperforming previous methods. The code is available at https://github.com/dqqcasia/st.
Multi-View Feature Representation for Dialogue Generation with Bidirectional Distillation
Authors: Feng Shaoxiong , Ren Xuancheng , Li Kan , Sun Xu
Published: September 2022

Neural dialogue models suffer from low-quality responses when interacted in practice, demonstrating difficulty in generalization beyond training data. Recently, knowledge distillation has been used to successfully regularize the student by transferring knowledge from the teacher. However, the teacher and the student are trained on the same dataset and tend to learn similar feature representations, whereas the most general knowledge should be found through differences. The finding of general knowledge is further hindered by the unidirectional distillation, as the student should obey the teacher and may discard some knowledge that is truly general but refuted by the teacher. To this end, we propose a novel training framework, where the learning of general knowledge is more in line with the idea of reaching consensus, i.e., finding common knowledge that is beneficial to different yet all datasets through diversified learning partners. Concretely, the training task is divided into a group of subtasks with the same number of students. Each student assigned to one subtask not only is optimized on the allocated subtask but also imitates multi-view feature representation aggregated from other students (i.e., student peers), which induces students to capture common knowledge among different subtasks and alleviates the over-fitting of students on the allocated subtasks. To further enhance generalization, we extend the unidirectional distillation to the bidirectional distillation that encourages the student and its student peers to co-evolve by exchanging complementary knowledge with each other. Empirical results and analysis demonstrate that our training framework effectively improves the model generalization without sacrificing training efficiency.
Sketch and Customize: A Counterfactual Story Generator
Authors: Hao Changying , Pang Liang , Lan Yanyan , Wang Yan , Guo Jiafeng , Cheng Xueqi
Published: September 2022

Recent text generation models are easy to generate relevant and fluent text for the given text, while lack of causal reasoning ability when we change some parts of the given text.
Counterfactual story rewriting is a recently proposed task to test the causal reasoning ability for text generation models, which requires a model to predict the corresponding story ending when the condition is modified to a counterfactual one. Previous works have shown that the traditional sequence-to-sequence model cannot well handle this problem, as it often captures some spurious correlations between the original and counterfactual endings, instead of the causal relations between conditions and endings.
To address this issue, we propose a sketch-and-customize generation model guided by the causality implicated in the conditions and endings. In the sketch stage, a skeleton is extracted by removing words which are conflict to the counterfactual condition, from the original ending. In the customize stage, a generation model is used to fill proper words in the skeleton under the guidance of the counterfactual condition. In this way, the obtained counterfactual ending is both relevant to the original ending and consistent with the counterfactual condition. Experimental results show that the proposed model generates much better endings, as compared with the traditional sequence-to-sequence model.
SMART: A Situation Model for Algebra Story Problems via Attributed Grammar
Authors: Hong Yining , Li Qing , Gong Ran , Ciao Daniel , Huang Siyuan , Zhu Song-Chun
Published: September 2022

Solving algebra story problems remains a challenging task in artificial intelligence, which requires a detailed understanding of real-world situations and a strong mathematical reasoning capability. Previous neural solvers of math word problems directly translate problem texts into equations, lacking an explicit interpretation of the situations, and often fail to handle more sophisticated situations. To address such limits of neural solvers, we introduce the concept of a situation model, which originates from psychology studies to represent the mental states of humans in problem-solving, and propose SMART, which adopts attributed grammar as the representation of situation models for algebra story problems. Specifically, we first train an information extraction module to extract nodes, attributes and relations from problem texts and then generate a parse graph based on a pre-defined attributed grammar. An iterative learning strategy is also proposed to further improve the performance of SMART. To study this task more rigorously, we carefully curate a new dataset named ASP6.6k. Experimental results on ASP6.6k show that the proposed model outperforms all previous neural solvers by a large margin, while preserving much better interpretability. To test these models' generalization capability, we also design an out-of-distribution (OOD) evaluation, in which problems are more complex than those in the training set. Our model exceeds state-of-the-art models by 17% in the OOD evaluation, demonstrating its superior generalization ability.
C2C-GenDA: Cluster-to-Cluster Generation for Data Augmentation of Slot Filling
Authors: Hou Yutai , Chen Sanyuan , Che Wanxiang , Chen Cheng , Liu Ting
Published: September 2022

Slot filling, a fundamental module of spoken language understanding, often suffers from insufficient quantity and diversity of training data. To remedy this, we propose a novel Cluster-to-Cluster generation framework for Data Augmentation (DA), named C2C-GenDA. It enlarges the training set by reconstructing existing utterances into alternative expressions while keeping semantic. Different from previous DA works that reconstruct utterances one by one independently, C2C-GenDA jointly encodes multiple existing utterances of the same semantics and simultaneously decodes multiple unseen expressions. Jointly generating multiple new utterances allows to consider the relations between generated instances and encourages diversity. Besides, encoding multiple existing utterances endows C2C with a wider view of existing expressions, helping to reduce generation that duplicates existing data. Experiments on ATIS and Snips datasets show that instances augmented by C2C-GenDA improve slot filling by 7.99 (11.9%↑) and 5.76 (13.6%↑) F-scores respectively, when there are only hundreds of training utterances. Code: https://github.com/Sanyuan-Chen/C2C-DA.
Few-shot Learning for Multi-label Intent Detection
Authors: Hou Yutai , Lai Yongkui , Wu Yushan , Che Wanxiang , Liu Ting
Published: September 2022

In this paper, we study the few-shot multi-label classification for user intent detection. For multi-label intent detection, state-of-the-art work estimates label-instance relevance scores and uses a threshold to select multiple associated intent labels. To determine appropriate thresholds with only a few examples, we first learn universal thresholding experience on data-rich domains, and then adapt the thresholds to certain few-shot domains with a calibration based on nonparametric learning. For better calculation of label-instance relevance score, we introduce label name embedding as anchor points in representation space, which refines representations of different classes to be well-separated from each other. Experiments on two datasets show that the proposed model significantly outperforms strong baselines in both one-shot and five-shot settings.
Entity Guided Question Generation with Contextual Structure and Sequence Information Capturing
Authors: Huang Qingbao , Fu Mingyi , Mo Linzhang , Cai Yi , Xu Jingyun , Li Pijian , Li Qing , Leung Ho-fung
Published: September 2022

Question generation is a challenging task and has attracted widespread attention in recent years. Although previous studies have made great progress, there are still two main shortcomings: First, previous work did not simultaneously capture the sequence information and structure information hidden in the context, which results in poor results of the generated questions. Second, the generated questions cannot be answered by the given context. To tackle these issues, we propose an entity guided question generation model with contextual structure information and sequence information capturing. We use a Graph Convolutional Network and a Bidirectional Long Short Term Memory Network to capture the structure information and sequence information of the context, simultaneously. In addition, to improve the answerability of the generated questions, we use an entity-guided approach to obtain question type from the answer, and jointly encode the answer and question type. Both automatic and manual metrics show that our model can generate comparable questions with state-of-the-art models. Our code is available at https://github.com/VISLANG-Lab/EGSS.
Story Ending Generation with Multi-Level Graph Convolutional Networks over Dependency Trees
Authors: Huang Qingbao , Mo Linzhang , Li Pijian , Cai Yi , Liu Qingguang , Wei Jielong , Li Qing , Leung Ho-fung
Published: September 2022

As an interesting and challenging task, story ending generation aims at generating a reasonable and coherent ending for a given story context. The key challenge of the task is to comprehend the context sufficiently and capture the hidden logic information effectively, which has not been well explored by most existing generative models. To tackle this issue, we propose a context-aware Multi-level Graph Convolutional Networks over Dependency Parse (MGCN-DP) trees to capture dependency relations and context clues more effectively. We utilize dependency parse trees to facilitate capturing relations and events in the context implicitly, and Multi-level Graph Convolutional Networks to update and deliver the representation crossing levels to obtain richer contextual information. Both automatic and manual evaluations show that our MGCN-DP can achieve comparable performance with state-of-the-art models. Our source code is available at https://github.com/VISLANG-Lab/MLGCN-DP.
Audio-Oriented Multimodal Machine Comprehension via Dynamic Inter- and Intra-modality Attention
Authors: Huang Zhiqi , Liu Fenglin , Wu Xian , Ge Shen , Wang Helin , Fan Wei , Zou Yuexian
Published: September 2022

While Machine Comprehension (MC) has attracted extensive research interests in recent years, existing approaches mainly belong to the category of Machine Reading Comprehension task which mines textual inputs (paragraphs and questions) to predict the answers (choices or text spans). However, there are a lot of MC tasks that accept audio input in addition to the textual input, e.g. English listening comprehension test. In this paper, we target the problem of Audio-Oriented Multimodal Machine Comprehension, and its goal is to answer questions based on the given audio and textual information. To solve this problem, we propose a Dynamic Inter- and Intra-modality Attention (DIIA) model to effectively fuse the two modalities (audio and textual). DIIA can work as an independent component and thus be easily integrated into existing MC models. Moreover, we further develop a Multimodal Knowledge Distillation (MKD) module to enable our multimodal MC model to accurately predict the answers based only on either the text or the audio. As a result, the proposed approach can handle various tasks including: Audio-Oriented Multimodal Machine Comprehension, Machine Reading Comprehension and Machine Listening Comprehension, in a single model, making fair comparisons possible between our model and the existing unimodal MC models. Experimental results and analysis prove the effectiveness of the proposed approaches. First, the proposed DIIA boosts the baseline models by up to 21.08% in terms of accuracy; Second, under the unimodal scenarios, the MKD module allows our multimodal MC model to significantly outperform the unimodal models by up to 18.87%, which are trained and tested with only audio or textual data.
EQG-RACE: Examination-Type Question Generation
Authors: Jia Xin , Zhou Wenjie , Sun Xu , Wu Yunfang
Published: September 2022

Question Generation (QG) is an essential component of the automatic intelligent tutoring systems, which aims to generate high-quality questions for facilitating the reading practice and assessments. However, existing QG technologies encounter several key issues concerning the biased and unnatural language sources of datasets which are mainly obtained from the Web (e.g. SQuAD). In this paper, we propose an innovative Examination-type Question Generation approach (EQG-RACE) to generate exam-like questions based on a dataset extracted from RACE. Two main strategies are employed in EQG-RACE for dealing with discrete answer information and reasoning among long contexts. A Rough Answer and Key Sentence Tagging scheme is utilized to enhance the representations of input. An Answer-guided Graph Convolutional Network (AG-GCN) is designed to capture structure information in revealing the inter-sentences and intra-sentence relations. Experimental results show a state-of-the-art performance of EQG-RACE, which is apparently superior to the baselines. In addition, our work has established a new QG prototype with a reshaped dataset and QG method, which provides an important benchmark for related research in future work. We will make our data and code publicly available for further research.
Merging Statistical Feature via Adaptive Gate for Improved Text Classification
Authors: Li Xianming , Li Zongxi , Xie Haoran , Li Qing
Published: September 2022

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 over labels, are not well exploited. Compared with external knowledge, the statistical features are deterministic and naturally compatible with corresponding tasks. In this paper, we propose an Adaptive Gate Network (AGN) to consolidate semantic representation with statistical features selectively. In particular, AGN encodes statistical features through a variational component and merges information via a well-designed valve mechanism. The valve adapts the information flow into the classifier according to the confidence of semantic features in decision making, which can facilitate training a robust classifier and can address the overfitting caused by using statistical features. Extensive experiments on datasets of various scales show that, by incorporating statistical information, AGN can improve the classification performance of CNN, RNN, Transformer, and Bert based models effectively. The experiments also indicate the robustness of AGN against adversarial attacks of manipulating statistical information.
An Efficient Transformer Decoder with Compressed Sub-layers
Authors: Li Yanyang , Lin Ye , Xiao Tong , Zhu Jingbo
Published: September 2022

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 could be simplified by compressing its sub-layers, the basic building block of Transformer, and achieves a higher parallelism. We thereby propose Compressed Attention Network, whose decoder layer consists of only one sub-layer instead of three. Extensive experiments on 14 WMT machine translation tasks show that our model is 1.42x faster with performance on par with a strong baseline. This strong baseline is already 2x faster than the widely used standard baseline without loss in performance.
Finding Sparse Structures for Domain Specific Neural Machine Translation
Authors: Liang Jianze , Zhao Chengqi , Wang Mingxuan , Qiu Xipeng , Li Lei
Published: September 2022

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 domain-specific sub-networks during fine-tuning on new domains. Prune-Tune alleviates the over-fitting and the degradation problem without model modification. Furthermore, Prune-Tune is able to sequentially learn a single network with multiple disjoint domain-specific sub-networks for multiple domains. Empirical experiment results show that Prune-Tune outperforms several strong competitors in the target domain test set without sacrificing the quality on the general domain in both single and multi-domain settings. The source code and data are available at https://github.com/ohlionel/Prune-Tune.
Infusing Multi-Source Knowledge with Heterogeneous Graph Neural Network for Emotional Conversation Generation
Authors: Liang Yunlong , Meng Fandong , Zhang Ying , Chen Yufeng , Xu Jinan , Zhou Jie
Published: September 2022

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, and then express suitable emotions according to our personalities, but these multiple types of information are insufficiently exploited in emotional conversation fields. To address this issue, we propose a heterogeneous graph-based model for emotional conversation generation. Specifically, we design a Heterogeneous Graph-Based Encoder to represent the conversation content (i.e., the dialogue history, its emotion flow, facial expressions, and speakers' personalities) with a heterogeneous graph neural network, and then predict suitable emotions for feedback. After that, we employ an Emotion-Personality-Aware Decoder to generate a response not only relevant to the conversation context but also with appropriate emotions, by taking the encoded graph representations, the predicted emotions from the encoder and the personality of the current speaker as inputs. Experimental results show that our model can effectively perceive emotions from multi-source knowledge and generate a satisfactory response, which significantly outperforms previous state-of-the-art models.
Faster Depth-Adaptive Transformers
Authors: Liu Yijin , Meng Fandong , Zhou Jie , Chen Yufeng , Xu Jinan
Published: September 2022

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 should continue or stop at each layer. As there is no specific supervision of depth selection, the halting unit may be under-optimized and inaccurate, which results in suboptimal and unstable performance when modeling sentences. In this paper, we get rid of the halting unit and estimate the required depths in advance, which yields a faster depth-adaptive model. Specifically, two approaches are proposed to explicitly measure the hardness of input words and estimate corresponding adaptive depth, namely 1) mutual information (MI) based estimation and 2) reconstruction loss based estimation. We conduct experiments on the text classification task with 24 datasets in various sizes and domains. Results confirm that our approaches can speed up the vanilla Transformer (up to 7x) while preserving high accuracy. Moreover, efficiency and robustness are significantly improved when compared with other depth-adaptive approaches.
Generating CCG Categories
Authors: Liu Yufang , Ji Tao , Wu Yuanbin , Lan Man
Published: September 2022

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. In this work, we propose to generate categories rather than classify them: each category is decomposed into a sequence of smaller atomic tags, and the tagger aims to generate the correct sequence. We show that with this finer view on categories, annotations of different categories could be shared and interactions with sentence contexts could be enhanced. The proposed category generator is able to achieve state-of-the-art tagging (95.5% accuracy) and parsing (89.8% labeled F1) performances on the standard CCGBank . Further-more, its performances on infrequent (even unseen) categories, out-of-domain texts and low resource language give promising results on introducing generation models to the general CCG analyses.
Bridging Towers of Multi-task Learning with a Gating Mechanism for Aspect-based Sentiment Analysis and Sequential Metaphor Identification
Authors: Mao Rui , Li Xiao
Published: September 2022

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 encoder to learn task-specific information. Previous works demonstrated that exchanging information between task-specific towers yielded extra gains. This is known as soft-parameter sharing MTL. In this paper, we propose a novel gating mechanism for the bridging of MTL towers. Our method is evaluated based on aspect-based sentiment analysis and sequential metaphor identification tasks. The experiments demonstrate that our method can yield better performance than the baselines on both tasks. Based on the same Transformer backbone, we compare our gating mechanism with other information transformation mechanisms, e.g., cross-stitch, attention and vanilla gating. The experiments show that our method also surpasses these baselines.
A Joint Training Dual-MRC Framework for Aspect Based Sentiment Analysis
Authors: Mao Yue , Shen Yi , Yu Chao , Cai Longjun
Published: September 2022

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., extracting aspect terms along with sentiment polarities or extracting the aspect and opinion terms pair-wisely. More recently, the triple extraction task has been proposed, i.e., extracting the (aspect term, opinion term, sentiment polarity) triples from a sentence. However, previous approaches fail to solve all subtasks in a unified end-to-end framework. In this paper, we propose a complete solution for ABSA. We construct two machine reading comprehension (MRC) problems, and solve all subtasks by joint training two BERT-MRC models with parameters sharing. We conduct experiments on these subtasks and results on several benchmark datasets demonstrate the effectiveness of our proposed framework, which significantly outperforms existing state-of-the-art methods.
The Heads Hypothesis: A Unifying Statistical Approach Towards Understanding Multi-Headed Attention in BERT
Authors: Pande Madhura , Budhraja Aakriti , Nema Preksha , Kumar Pratyush , Khapra Mitesh M.
Published: September 2022

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), block (tokens in the same sentence) and delimiter (the special [CLS], [SEP] tokens). There are two main challenges with existing methods for classification: (a) there are no standard scores across studies or across functional roles, and (b) these scores are often average quantities measured across sentences without capturing statistical significance. In this work, we formalize a simple yet effective score that generalizes to all the roles of attention heads and employs hypothesis testing on this score for robust inference. This provides us the right lens to systematically analyze attention heads and confidently comment on many commonly posed questions on analyzing the BERT model. In particular, we comment on the co-location of multiple functional roles in the same attention head, the distribution of attention heads across layers, and effect of fine-tuning for specific NLP tasks on these functional roles. The code is made publicly available at https://github.com/iitmnlp/heads-hypothesis
Copy That! Editing Sequences by Copying Spans
Authors: Panthaplackel Sheena , Allamanis Miltiadis , Brockschmidt Marc
Published: September 2022

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 unchanged token. We present an extension of seq2seq models capable of copying entire spans of the input to the output in one step, greatly reducing the number of decisions required during inference. This extension means that there are now many ways of generating the same output, which we handle by deriving a new objective for training and a variation of beam search for inference that explicitly handles this problem. In our experiments on a range of editing tasks of natural language and source code, we show that our new model consistently outperforms simpler baselines.
A Simple and Effective Self-Supervised Contrastive Learning Framework for Aspect Detection
Authors: Shi Tian , Li Liuqing , Wang Ping , Reddy Chandan K.
Published: September 2022

Unsupervised aspect detection (UAD) aims at automatically extracting interpretable aspects and identifying aspect-specific segments (such as sentences) from online reviews. However, recent deep learning based topic models, specifically aspect-based autoencoder, suffer from several problems such as extracting noisy aspects and poorly mapping aspects discovered by models to the aspects of interest. To tackle these challenges, in this paper, we first propose a self-supervised contrastive learning framework and an attention-based model equipped with a novel smooth self-attention (SSA) module for the UAD task in order to learn better representations for aspects and review segments. Secondly, we introduce a high-resolution selective mapping (HRSMap) method to efficiently assign aspects discovered by the model to the aspects of interest. We also propose using a knowledge distillation technique to further improve the aspect detection performance. Our methods outperform several recent unsupervised and weakly supervised approaches on publicly available benchmark user review datasets. Aspect interpretation results show that extracted aspects are meaningful, have a good coverage, and can be easily mapped to aspects of interest. Ablation studies and attention weight visualization also demonstrate effectiveness of SSA and the knowledge distillation method.
Improving Commonsense Causal Reasoning by Adversarial Training and Data Augmentation
Authors: Staliūnaitė Ieva , Gorinski Philip John , Iacobacci Ignacio
Published: September 2022

Determining the plausibility of causal relations between clauses is a commonsense reasoning task that requires complex inference ability. The general approach to this task is to train a large pretrained language model on a specific dataset. However, the available training data for the task is often scarce, which leads to instability of model training or reliance on the shallow features of the dataset. This paper presents a number of techniques for making models more robust in the domain of causal reasoning. Firstly, we perform adversarial training by generating perturbed inputs through synonym substitution. Secondly, based on a linguistic theory of discourse connectives, we perform data augmentation using a discourse parser for detecting causally linked clauses in large text, and a generative language model for generating distractors. Both methods boost model performance on the Choice of Plausible Alternatives (COPA) dataset, as well as on a Balanced COPA dataset, which is a modified version of the original data that has been developed to avoid superficial cues, leading to a more challenging benchmark. We show a statistically significant improvement in performance and robustness on both datasets, even with only a small number of additionally generated data points.
Re-TACRED: Addressing Shortcomings of the TACRED Dataset
Authors: Stoica George , Platanios Emmanouil Antonios , Poczos Barnabas
Published: September 2022

TACRED is one of the largest and most widely used sentence-level relation extraction datasets. Proposed models that are evaluated using this dataset consistently set new state-of-the-art performance. However, they still exhibit large error rates despite leveraging external knowledge and unsupervised pretraining on large text corpora. A recent study suggested that this may be due to poor dataset quality. The study observed that over 50% of the most challenging sentences from the development and test sets are incorrectly labeled and account for an average drop of 8% f1-score in model performance. However, this study was limited to a small biased sample of 5k (out of a total of 106k) sentences, substantially restricting the generalizability and broader implications of its findings. In this paper, we address these shortcomings by: (i) performing a comprehensive study over the whole TACRED dataset, (ii) proposing an improved crowdsourcing strategy and deploying it to re-annotate the whole dataset, and (iii) performing a thorough analysis to understand how correcting the TACRED annotations affects previously published results. After verification, we observed that 23.9% of TACRED labels are incorrect. Moreover, evaluating several models on our revised dataset yields an average f1-score improvement of 14.3% and helps uncover significant relationships between the different models (rather than simply offsetting or scaling their scores by a constant factor). Finally, aside from our analysis we also release Re-TACRED, a new completely re-annotated version of the TACRED dataset that can be used to perform reliable evaluation of relation extraction models.
NaturalConv: A Chinese Dialogue Dataset Towards Multi-turn Topic-driven Conversation
Authors: Wang Xiaoyang , Li Chen , Zhao Jianqiao , Yu Dong
Published: September 2022

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 average turn number of 20.1. These conversations contain in-depth discussions on related topics or widely natural transition between multiple topics. We believe either way is normal for human conversation. To facilitate the research on this corpus, we provide results of several benchmark models. Comparative results show that for this dataset, our current models are not able to provide significant improvement by introducing background knowledge/topic. Therefore, the proposed dataset should be a good benchmark for further research to evaluate the validity and naturalness of multi-turn conversation systems. Our dataset is available at https://ai.tencent.com/ailab/nlp/dialogue/#datasets.
Context-Guided BERT for Targeted Aspect-Based Sentiment Analysis
Authors: Wu Zhengxuan , Ong Desmond C.
Published: September 2022

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 associated with each target-aspect pair. In this paper, we investigate whether adding context to self-attention models improves performance on (T)ABSA. We propose two variants of Context-Guided BERT (CG-BERT) that learn to distribute attention under different contexts. We first adapt a context-aware Transformer to produce a CG-BERT that uses context-guided softmax-attention. Next, we propose an improved Quasi-Attention CG-BERT model that learns a compositional attention that supports subtractive attention. We train both models with pretrained BERT on two (T)ABSA datasets: SentiHood and SemEval-2014 (Task 4). Both models achieve new state-of-the-art results with our QACG-BERT model having the best performance. Furthermore, we provide analyses of the impact of context in the our proposed models. Our work provides more evidence for the utility of adding context-dependencies to pretrained self-attention-based language models for context-based natural language tasks.
Enabling Fast and Universal Audio Adversarial Attack Using Generative Model
Authors: Xie Yi , Li Zhuohang , Shi Cong , Liu Jian , Chen Yingying , Yuan Bo
Published: September 2022

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 perturbations. These idealized assumptions, however, make the existing audio adversarial attacks mostly impossible to be launched in a timely fashion in practice (e.g., playing unnoticeable adversarial perturbations along with user's streaming input). To overcome these limitations, in this paper we propose fast audio adversarial perturbation generator (FAPG), which uses generative model to generate adversarial perturbations for the audio input in a single forward pass, thereby drastically improving the perturbation generation speed. Built on the top of FAPG, we further propose universal audio adversarial perturbation generator (UAPG), a scheme to craft universal adversarial perturbation that can be imposed on arbitrary benign audio input to cause misclassification. Extensive experiments on DNN-based audio systems show that our proposed FAPG can achieve high success rate with up to 214X speedup over the existing audio adversarial attack methods. Also our proposed UAPG generates universal adversarial perturbations that can achieve much better attack performance than the state-of-the-art solutions.
Keyword-Guided Neural Conversational Model
Authors: Zhong Peixiang , Liu Yong , Wang Hao , Miao Chunyan
Published: September 2022

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 psychotherapy. The dominant paradigm for tackling this problem is to 1) train a next-turn keyword classifier, and 2) train a keyword-augmented response retrieval model. However, existing approaches in this paradigm have two limitations: 1) the training and evaluation datasets for next-turn keyword classification are directly extracted from conversations without human annotations, thus, they are noisy and have low correlation with human judgements, and 2) during keyword transition, the agents solely rely on the similarities between word embeddings to move closer to the target keyword, which may not reflect how humans converse. In this paper, we assume that human conversations are grounded on commonsense and propose a keyword-guided neural conversational model that can leverage external commonsense knowledge graphs (CKG) for both keyword transition and response retrieval. Automatic evaluations suggest that commonsense improves the performance of both next-turn keyword prediction and keyword-augmented response retrieval. In addition, both self-play and human evaluations show that our model produces responses with smoother keyword transition and reaches the target keyword faster than competitive baselines.
CARE: Commonsense-Aware Emotional Response Generation with Latent Concepts
Authors: Zhong Peixiang , Wang Di , Li Pengfei , Zhang Chen , Wang Hao , Miao Chunyan
Published: September 2022

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. In this paper, we hypothesize that combining rationality and emotion into conversational agents can improve response quality. To test the hypothesis, we focus on one fundamental aspect of rationality, i.e., commonsense, and propose CARE, a novel model for commonsense-aware emotional response generation. Specifically, we first propose a framework to learn and construct commonsense-aware emotional latent concepts of the response given an input message and a desired emotion. We then propose three methods to collaboratively incorporate the latent concepts into response generation. Experimental results on two large-scale datasets support our hypothesis and show that our model can produce more accurate and commonsense-aware emotional responses and achieve better human ratings than state-of-the-art models that only specialize in one aspect.
Real-time Tropical Cyclone Intensity Estimation by Handling Temporally Heterogeneous Satellite Data
Authors: Chen Boyo , Chen Buo-Fu , Chen Yun-Nung
Published: September 2022

Analyzing big geophysical observational data collected by multiple advanced sensors on various satellite platforms promotes our understanding of the geophysical system. For instance, convolutional neural networks (CNN) have achieved great success in estimating tropical cyclone (TC) intensity based on satellite data with fixed temporal frequency (e.g., ~3 h). However, to achieve more timely (under 30 min) and accurate TC intensity estimates, a deep learning model is demanded to handle temporally-heterogeneous satellite observations.
Specifically, infrared (IR1) and water vapor (WV) images are available within every 15 minute period, while passive microwave rain rate (PMW) is available about every 3 hours. Meanwhile, the visible (VIS) channel is severely affected by noise and sunlight intensity, making it difficult to be utilized.
Therefore, we propose a novel framework that combines generative adversarial network (GAN) with CNN. The model utilizes all data during the training phase including VIS and PMW information and eventually uses only the high-frequent IR1 and WV data for providing intensity estimates during the predicting phase.
Experimental results demonstrate that the hybrid GAN-CNN framework achieves comparable precision to the state-of-the-art models, while possessing the capability of increasing the maximum estimation frequency from 3 hours to less than 15 minutes.
Please visit https://github.com/BoyoChen/CNN-GAN-TC for codes and implementation details.
Harnessing Social Media to Identify Homeless Youth At-Risk of Substance Use
Authors: Dou Zi-Yi , Barman-Adhikari Anamika , Fang Fei , Yadav Amulya
Published: September 2022

Homeless youth are a highly vulnerable population and report highly elevated rates of substance use. Prior work on mitigating substance use among homeless youth has primarily relied on survey data to get information about substance use among homeless youth, which can then be used to inform the design of targeted intervention programs. However, such survey data is often onerous to collect, is limited by its reliance on self-reports and retrospective recall, and quickly becomes dated. The advent of social media has provided us with an important data source for understanding the health behaviors of homeless youth. In this paper, we target this specific population and demonstrate how to detect substance use based on texts from social media. We collect ~135K Facebook posts and comments together with survey responses from a group of homeless youth and use this data to build novel substance use detection systems with machine learning and natural language processing techniques. Experimental results show that our proposed methods achieve ROC-AUC scores of ~0.77 on identifying certain kinds of substance use among homeless youth using Facebook conversations only, and ROC-AUC scores of ~0.83 when combined with answers to four survey questions that are not about their demographic characteristics or substance use. Furthermore, we investigate connections between the characteristics of people's Facebook posts and substance use and provide insights about the problem.
Predicting Flashover Occurrence using Surrogate Temperature Data
Authors: Fu Eugene Yujun , Tam Wai Cheong , Wang Jun , Peacock Richard , Reneke Paul A , Ngai Grace , Leong Hong Va , Cleary Thomas
Published: September 2022

Fire fighter fatalities and injuries in the U.S. remain too high and fire fighting too hazardous. Until now, fire fighters rely only on their experience to avoid life-threatening fire events, such as flashover. In this paper, we describe the development of a flashover prediction model which can be used to warn fire fighters before flashover occurs. Specifically, we consider the use of a fire simulation program to generate a set of synthetic data and an attention-based bidirectional long short-term memory to learn the complex relationships between temperature signals and flashover conditions. We first validate the fire simulation program with temperature measurements obtained from full-scale fire experiments. Then, we generate a set of synthetic temperature data which account for the realis-tic fire and vent opening conditions in a multi-compartment structure. Results show that our proposed method achieves promising performance for prediction of flashover even when temperature data is completely lost in the room of fire origin. It is believed that the flashover prediction model can facilitate the transformation of fire fighting tactics from traditional experience-based decision marking to data-driven decision marking and reduce fire fighter deaths and injuries.
Mitigating Political Bias in Language Models through Reinforced Calibration
Authors: Liu Ruibo , Jia Chenyan , Wei Jason , Xu Guangxuan , Wang Lili , Vosoughi Soroush
Published: September 2022

Current large-scale language models can be politically biased as a result of the data they are trained on, potentially causing serious problems when they are deployed in real-world settings. In this paper, we describe metrics for measuring political bias in GPT-2 generation and propose a reinforcement learning (RL) framework for mitigating political biases in generated text. By using rewards from word embeddings or a classifier, our RL framework guides debiased generation without having access to the training data or requiring the model to be retrained. In empirical experiments on three attributes sensitive to political bias (gender, location, and topic), our methods reduced bias according to both our metrics and human evaluation, while maintaining readability and semantic coherence.
HateXplain: A Benchmark Dataset for Explainable Hate Speech Detection
Authors: Mathew Binny , Saha Punyajoy , Yimam Seid Muhie , Biemann Chris , Goyal Pawan , Mukherjee Animesh
Published: September 2022

Hate speech is a challenging issue plaguing the online social media. While better models for hate speech detection are continuously being developed, there is little research on the bias and interpretability aspects of hate speech. In this paper, we introduce HateXplain, the first benchmark hate speech dataset covering multiple aspects of the issue. Each post in our dataset is annotated from three different perspectives: the basic, commonly used 3-class classification (i.e., hate, offensive or normal), the target community (i.e., the community that has been the victim of hate speech/offensive speech in the post), and the rationales, i.e., the portions of the post on which their labelling decision (as hate, offensive or normal) is based. We utilize existing state-of-the-art models and observe that even models that perform very well in classification do not score high on explainability metrics like model plausibility and faithfulness. We also observe that models, which utilize the human rationales for training, perform better in reducing unintended bias towards target communities. We have made our code and dataset public for other researchers.
Evidence Aware Neural Pornographic Text Identification for Child Protection
Authors: Song Kaisong , Kang Yangyang , Gao Wei , Gao Zhe , Sun Changlong , Liu Xiaozhong
Published: September 2022

Identifying pornographic text online is practically useful to protect children from access to such adult content. However, some authors may intentionally avoid using sensitive words in their pornographic texts to take advantage of the lack of human audits. Without prior knowledge guidance, real semantics of such pornographic text is difficult to understand by existing methods due to its high context-sensitivity and heavy usage of figurative language, which brings huge challenges to the porn detection systems used in social media platforms. In this paper, we approach to the problem as a document-level porn identification task by locating and integrating sentence-level evidence and propose a novel Evidence-Aware Neural Porn Classification (eNPC) model. Specifically, we first propose a basic model which locates porn indicative sentences in the document with a multiple instance learning model, and then aggregate the sentence-level evidence to induce document label with self-attention mechanism. Moreover, we consider label dependencies within local context. Finally, we further enhance the sentence representation with prior knowledge produced by an automatic porn lexicon construction strategy. Extensive experimental results show that our model exhibits consistent superiority over competitors on two real-world Chinese novel datasets and an English story dataset.