GenAI4ELab/papercli-papers huggingface dataset:
Sequence Generation with Label Augmentation for Relation Extraction
Authors: Bo Li; Dingyao Yu; Wei Ye; Jinglei Zhang; Shikun Zhang
Venue: AAAI 2023 (main)

Abstract: Sequence generation demonstrates promising performance in recent information extraction efforts, by incorporating large-scale pre-trained Seq2Seq models. This paper investigates the merits of employing sequence generation in relation extraction, finding that with relation names or synonyms as generation targets, their textual semantics and the correlation (in terms of word sequence pattern) among them affect model performance. We then propose Relation Extraction with Label Augmentation (RELA), a Seq2Seq model with automatic label augmentation for RE. By saying label augmentation, we mean prod semantically synonyms for each relation name as the generation target. Besides, we present an in-depth analysis of the Seq2Seq model's behavior when dealing with RE. Experimental results show that RELA achieves competitive results compared with previous methods on four RE datasets.
Reviewing Labels: Label Graph Network with Top-k Prediction Set for Relation Extraction
Authors: Bo Li; Wei Ye; Jinglei Zhang; Shikun Zhang
Venue: AAAI 2023 (main)

Abstract: The typical way for relation extraction is fine-tuning large pre-trained language models on task-specific datasets, then selecting the label with the highest probability of the output
distribution as the final prediction. However, the usage of the Top-k prediction set for a given sample is commonly overlooked. In this paper, we first reveal that the Top-k prediction
set of a given sample contains useful information for predicting the correct label. To effectively utilizes the Top-k prediction set, we propose Label Graph Network with Top-k Prediction Set, termed as KLG. Specifically, for a given sample, we build a label graph to review candidate labels in the Top-k prediction set and learn the connections between them. We also design a dynamic k selection mechanism to learn more powerful and discriminative relation representation. Our experiments show that KLG achieves the best performances on three relation extraction datasets. Moreover, we observe thatKLG is more effective in dealing with long-tailed classes.
Online Noisy Continual Relation Learning
Authors: Guozheng Li; Peng Wang; Qiqing Luo; Yanhe Liu; Wenjun Ke
Venue: AAAI 2023 (main)

Abstract: Recent work for continual relation learning has achieved remarkable progress. However, most existing methods only focus on tackling catastrophic forgetting to improve performance in the existing setup, while continually learning relations in the real-world must overcome many other challenges. One is that the data possibly comes in an online streaming fashion with data distributions gradually changing and without distinct task boundaries. Another is that noisy labels are inevitable in real-world, as relation samples may be contaminated by label inconsistencies or labeled with distant supervision. In this work, therefore, we propose a novel continual relation learning framework that simultaneously addresses both online and noisy relation learning challenges. Our framework contains three key modules: (i) a sample separated online purifying module that divides the online data stream into clean and noisy samples, (ii) a self-supervised online learning module that circumvents inferior training signals caused by noisy data, and (iii) a semi-supervised offline finetuning module that ensures the participation of both clean and noisy samples. Experimental results on FewRel, TACRED and NYT-H with real-world noise demonstrate that our framework greatly outperforms the combinations of the state-of-the-art online continual learning and noisy label learning methods.
RESDSQL: Decoupling Schema Linking and Skeleton Parsing for Text-to-SQL
Authors: Haoyang Li; Jing Zhang; Cuiping Li; Hong Chen
Venue: AAAI 2023 (main)

Abstract: One of the recent best attempts at Text-to-SQL is the pre-trained language model. Due to the structural property of the SQL queries, the seq2seq model takes the responsibility of parsing both the schema items (i.e., tables and columns) and the skeleton (i.e., SQL keywords). Such coupled targets increase the difficulty of parsing the correct SQL queries especially when they involve many schema items and logic operators. This paper proposes a ranking-enhanced encoding and skeleton-aware decoding framework to decouple the schema linking and the skeleton parsing. Specifically, for a seq2seq encoder-decode model, its encoder is injected by the most relevant schema items instead of the whole unordered ones, which could alleviate the schema linking effort during SQL parsing, and its decoder first generates the skeleton and then the actual SQL query, which could implicitly constrain the SQL parsing. We evaluate our proposed framework on Spider and its three robustness variants: Spider-DK, Spider-Syn, and Spider-Realistic. The experimental results show that our framework delivers promising performance and robustness. Our code is available at https://github.com/RUCKBReasoning/RESDSQL.
Graphix-T5: Mixing Pre-trained Transformers with Graph-Aware Layers for Text-to-SQL Parsing
Authors: Jinyang Li; Binyuan Hui; Reynold Cheng; Bowen Qin; Chenhao Ma; Nan Huo; Fei Huang; Wenyu Du; Luo Si; Yongbin Li
Venue: AAAI 2023 (main)

Abstract: The task of text-to-SQL parsing, which aims at converting natural language questions into executable SQL queries, has garnered increasing attention in recent years. One of the major challenges in text-to-SQL parsing is domain generalization, i.e., how to generalize well to unseen databases. Recently, the pre-trained text-to-text transformer model, namely T5, though not specialized for text-to-SQL parsing, has achieved state-of-the-art performance on standard benchmarks targeting domain generalization. In this work, we explore ways to further augment the pre-trained T5 model with specialized components for text-to-SQL parsing. Such components are expected to introduce structural inductive bias into text-to-SQL parsers thus improving the model’s capacity on (potentially multi-hop) reasoning, which is critical for generating structure-rich SQLs. To this end, we propose a new architecture GRAPHIX-T5, a mixed model with the standard pre-trained transformer model augmented by specially-designed graph-aware layers. Extensive experiments and analysis demonstrate the effectiveness of GRAPHIX-T5 across four text-to-SQL benchmarks: SPIDER, SYN, REALISTIC and DK. GRAPHIX-T5 surpasses all other T5-based parsers with a significant margin, achieving new state-of-the-art performance. Notably, GRAPHIX-T5-large reaches performance superior to the original T5-large by 5.7% on exact match (EM) accuracy and 6.6% on execution accuracy (EX). This even outperforms the T5-3B by 1.2% on EM and 1.5% on EX
Compressed Heterogeneous Graph for Abstractive Multi-Document Summarization
Authors: Miao Li; Jianzhong Qi; Jey Han Lau
Venue: AAAI 2023 (main)

Abstract: Multi-document summarization (MDS) aims to generate a summary for a number of related documents. We propose HGSum — an MDS model that extends an encoder-decoder architecture to incorporate a heterogeneous graph to represent different semantic units (e.g., words and sentences) of the documents. This contrasts with existing MDS models which do not consider different edge types of graphs and as such do not capture the diversity of relationships in the documents. To preserve only key information and relationships of the documents in the heterogeneous graph, HGSum uses graph pooling to compress the input graph. And to guide HGSum to learn the compression, we introduce an additional objective that maximizes the similarity between the compressed graph and the graph constructed from the ground-truth summary during training. HGSum is trained end-to-end with the graph similarity and standard cross-entropy objectives. Experimental results over Multi-News, WCEP-100, and Arxiv show that HGSum outperforms state-of-the-art MDS models. The code for our model and experiments is available at: https://github.com/oaimli/HGSum.
TrOCR: Transformer-Based Optical Character Recognition with Pre-trained Models
Authors: Minghao Li; Tengchao Lv; Jingye Chen; Lei Cui; Yijuan Lu; Dinei Florencio; Cha Zhang; Zhoujun Li; Furu Wei
Venue: AAAI 2023 (main)

Abstract: Text recognition is a long-standing research problem for document digitalization. Existing approaches are usually built based on CNN for image understanding and RNN for char-level text generation. In addition, another language model is usually needed to improve the overall accuracy as a post-processing step. In this paper, we propose an end-to-end text recognition approach with pre-trained image Transformer and text Transformer models, namely TrOCR, which leverages the Transformer architecture for both image understanding and wordpiece-level text generation. The TrOCR model is simple but effective, and can be pre-trained with large-scale synthetic data and fine-tuned with human-labeled datasets. Experiments show that the TrOCR model outperforms the current state-of-the-art models on the printed, handwritten and scene text recognition tasks. The TrOCR models and code are publicly available at https://aka.ms/trocr.
Mitigating Negative Style Transfer in Hybrid Dialogue System
Authors: Shimin Li; Qinyuan Cheng; Linyang Li; Xipeng Qiu
Venue: AAAI 2023 (main)

Abstract: As the functionality of dialogue systems evolves, hybrid dialogue systems that accomplish user-specific goals and participate in open-topic chitchat with users are attracting growing attention. Existing research learns both tasks concurrently utilizing a multi-task fusion technique but ignores the negative transfer phenomenon induced by the unique textual style differences. Therefore, contrastive learning based on the latent variable model is used to decouple the various textual genres in the latent space. We devise supervised and self-supervised positive and negative sample constructions for diverse datasets. In addition, to capitalize on the style information contained in the decoupled latent variables, we employ a style prefix that incorporates latent variables further to control the generation of responses with varying styles. We performed extensive experiments on three dialogue datasets, including a hybrid dialogue dataset and two task-oriented dialogue datasets. The experimental results demonstrate that our method can mitigate the negative style transfer issue and achieves state-of-the-art performance on multiple dialogue datasets.
Low Resource Quantitative Information Extraction via Structure Searching and Prefix-Based Text Generation
Authors: Tongliang Li; Zixiang Wang; Zhoujun Li
Venue: AAAI 2023 (main)

Abstract: Quantitative information plays an important part in the financial and data analysis areas. Prior work relied on pattern-matching methods and complex hand-crafted rules to extract quantitative information due to the lack of labeled data. Such methods can be unstable and difficult to scale to the open domain. In this paper, we study quantitative information extraction in the low-resource setting. We propose a search-based approach by searching from the syntactic structures to acquire basic training data. The search process is simple yet effective. Then, a prefix-based text-to-text generation method is employed to extract the quantitative information. The prefix design can fully leverage pre-trained language models for text generation to serve the information extraction purpose. Experimental results show that our approaches achieves high performance with a limited amount of labeled data. The extraction result could further boost the performance of other tasks such as quantitative reasoning.
SKIER: A Symbolic Knowledge Integrated Model for Conversational Emotion Recognition
Authors: Wei Li; Luyao Zhu; Rui Mao; Erik Cambria
Venue: AAAI 2023 (main)

Abstract: Emotion recognition in conversation (ERC) has received increasing attention from the research community. However, the ERC task is challenging, largely due to the complex and unstructured properties of multi-party conversations. Besides, the majority of daily dialogues take place in a specific context or circumstance, which requires rich external knowledge to understand the background of a certain dialogue. In this paper, we address these challenges by explicitly modeling the discourse relations between utterances and incorporating symbolic knowledge into multi-party conversations. We first introduce a dialogue parsing algorithm into ERC and further improve the algorithm through a transfer learning method. Moreover, we leverage different symbolic knowledge graph relations to learn knowledge-enhanced features for the ERC task. Extensive experiments on three benchmarks demonstrate that both dialogue structure graphs and symbolic knowledge are beneficial to the model performance on the task. Additionally, experimental results indicate that the proposed model surpasses baseline models on several indices.
PGSS: Pitch-Guided Speech Separation
Authors: Xiang Li; Yiwen Wang; Yifan Sun; Xihong Wu; Jing Chen
Venue: AAAI 2023 (main)

Abstract: Monaural speech separation aims to separate concurrent speakers from a single-microphone mixture recording. Inspired by the effect of pitch priming in auditory scene analysis (ASA) mechanisms, a novel pitch-guided speech separation framework is proposed in this work. The prominent advantage of this framework is that both the permutation problem and the unknown speaker number problem existing in general models can be avoided by using pitch contours as the primary means to guide the target speaker. In addition, adversarial training is applied, instead of a traditional time-frequency mask, to improve the perceptual quality of separated speech. Specifically, the proposed framework can be divided into two phases: pitch extraction and speech separation. The former aims to extract pitch contour candidates for each speaker from the mixture, modeling the bottom-up process in ASA mechanisms. Any pitch contour can be selected as the condition in the second phase to separate the corresponding speaker, where a conditional generative adversarial network (CGAN) is applied. The second phase models the effect of pitch priming in ASA. Experiments on the WSJ0-2mix corpus reveal that the proposed approaches can achieve higher pitch extraction accuracy and better separation performance, compared to the baseline models, and have the potential to be applied to SOTA architectures.
DyRRen: A Dynamic Retriever-Reranker-Generator Model for Numerical Reasoning over Tabular and Textual Data
Authors: Xiao Li; Yin Zhu; Sichen Liu; Jiangzhou Ju; Yuzhong Qu; Gong Cheng
Venue: AAAI 2023 (main)

Abstract: Numerical reasoning over hybrid data containing tables and long texts has recently received research attention from the AI community. To generate an executable reasoning program consisting of math and table operations to answer a question, state-of-the-art methods use a retriever-generator pipeline. However, their retrieval results are static, while different generation steps may rely on different sentences. To attend to the retrieved information that is relevant to each generation step, in this paper, we propose DyRRen, an extended retriever-reranker-generator framework where each generation step is enhanced by a dynamic reranking of retrieved sentences. It outperforms existing baselines on the FinQA dataset.
Heterogeneous-Branch Collaborative Learning for Dialogue Generation
Authors: Yiwei Li; Shaoxiong Feng; Bin Sun; Kan Li
Venue: AAAI 2023 (main)

Abstract: With the development of deep learning, advanced dialogue generation methods usually require a greater amount of computational resources. One promising approach to obtaining a high-performance and lightweight model is knowledge distillation, which relies heavily on the pre-trained powerful teacher. Collaborative learning, also known as online knowledge distillation, is an effective way to conduct one-stage group distillation in the absence of a well-trained large teacher model. However, previous work has a severe branch homogeneity problem due to the same training objective and the independent identical training sets.  To alleviate this problem, we consider the dialogue attributes in the training of network branches. Each branch learns the attribute-related features based on the selected subset. Furthermore, we propose a dual group-based knowledge distillation method, consisting of positive distillation and negative distillation, to further diversify the features of different branches in a steadily and interpretable way. The proposed approach significantly improves branch heterogeneity and outperforms state-of-the-art collaborative learning methods on two widely used open-domain dialogue datasets.
Learning to Know Myself: A Coarse-to-Fine Persona-Aware Training Framework for Personalized Dialogue Generation
Authors: Yunpeng Li; Yue Hu; Yajing Sun; Luxi Xing; Ping Guo; Yuqiang Xie; Wei Peng
Venue: AAAI 2023 (main)

Abstract: A critical challenge for open-domain dialogue agents is to generate persona-relevant and consistent responses. Due to the nature of persona sparsity in conversation scenarios, previous persona-based dialogue agents trained with Maximum Likelihood Estimation tend to overlook the given personas and generate responses irrelevant or inconsistent with personas. To address this problem, we propose a two-stage coarse-to-fine persona-aware training framework to improve the persona consistency of a dialogue agent progressively. Specifically, our framework first trains the dialogue agent to answer the constructed persona-aware questions, making it highly sensitive to the personas to generate persona-relevant responses. Then the dialogue agent is further trained with a contrastive learning paradigm by explicitly perceiving the difference between the consistent and the generated inconsistent responses, forcing it to pay more attention to the key persona information to generate consistent responses. By applying our proposed training framework to several representative baseline models, experimental results show significant boosts on both automatic and human evaluation metrics, especially the consistency of generated responses.
WIERT: Web Information Extraction via Render Tree
Authors: Zimeng Li; Bo Shao; Linjun Shou; Ming Gong; Gen Li; Daxin Jiang
Venue: AAAI 2023 (main)

Abstract: Web information extraction (WIE) is a fundamental problem in web document understanding, with a significant impact on various applications. Visual information plays a crucial role in WIE tasks as the nodes containing relevant information are often visually distinct, such as being in a larger font size or having a brighter color, from the other nodes. However, rendering visual information of a web page can be computationally expensive. Previous works have mainly focused on the Document Object Model (DOM) tree, which lacks visual information. To efficiently exploit visual information, we propose leveraging the render tree, which combines the DOM tree and Cascading Style Sheets Object Model (CSSOM) tree, and contains not only content and layout information but also rich visual information at a little additional acquisition cost compared to the DOM tree. In this paper, we present WIERT, a method that effectively utilizes the render tree of a web page based on a pretrained language model. We evaluate WIERT on the Klarna product page dataset, a manually labeled dataset of renderable e-commerce web pages, demonstrating its effectiveness and robustness.
STAGE: Span Tagging and Greedy Inference Scheme for Aspect Sentiment Triplet Extraction
Authors: Shuo Liang; Wei Wei; Xian-Ling Mao; Yuanyuan Fu; Rui Fang; Dangyang Chen
Venue: AAAI 2023 (main)

Abstract: Aspect Sentiment Triplet Extraction (ASTE) has become an emerging task in sentiment analysis research, aiming to extract triplets of the aspect term, its corresponding opinion term, and its associated sentiment polarity from a given sentence. Recently, many neural networks based models with different tagging schemes have been proposed, but almost all of them have their limitations: heavily relying on 1) prior assumption that each word is only associated with a single role (e.g., aspect term, or opinion term, etc. ) and 2) word-level interactions and treating each opinion/aspect as a set of independent words. Hence, they perform poorly on the complex ASTE task, such as a word associated with multiple roles or an aspect/opinion term with multiple words. Hence, we propose a novel approach, Span TAgging and Greedy infErence (STAGE), to extract sentiment triplets in span-level, where each span may consist of multiple words and play different roles simultaneously. To this end, this paper formulates the ASTE task as a multi-class span classification problem. Specifically, STAGE generates more accurate aspect sentiment triplet extractions via exploring span-level information and constraints, which consists of two components, namely, span tagging scheme and greedy inference strategy. The former tag all possible candidate spans based on a newly-defined tagging set. The latter retrieves the aspect/opinion term with the maximum length from the candidate sentiment snippet to output sentiment triplets. Furthermore, we propose a simple but effective model based on the STAGE, which outperforms the state-of-the-arts by a large margin on four widely-used datasets. Moreover, our STAGE can be easily generalized to other pair/triplet extraction tasks, which also demonstrates the superiority of the proposed scheme STAGE.
Generalizing Math Word Problem Solvers via Solution Diversification
Authors: Zhenwen Liang; Jipeng Zhang; Lei Wang; Yan Wang; Jie Shao; Xiangliang Zhang
Venue: AAAI 2023 (main)

Abstract: Current math word problem (MWP) solvers are usually Seq2Seq models  trained by the (one-problem; one-solution) pairs, each of which is made of a problem description and a solution showing reasoning flow to get the correct answer. However, one MWP problem naturally has multiple solution equations. The training of an MWP solver with (one-problem; one-solution) pairs excludes other correct solutions, and thus limits the generalizability of the MWP solver. One feasible solution to this limitation is to augment multiple solutions to a given problem. However, it is difficult to collect diverse and accurate augment solutions through human efforts. In this paper, we design a new training framework for an MWP solver by introducing a solution buffer and a solution discriminator. The buffer includes solutions generated by an MWP solver to encourage  the training data diversity.  The discriminator controls the quality of buffered solutions to participate in training. Our framework is flexibly applicable to a wide setting of fully, semi-weakly and weakly supervised training for all Seq2Seq MWP solvers. We conduct extensive experiments on a benchmark dataset Math23k and a new dataset named Weak12k, and show that our framework improves the performance of various MWP solvers under different settings by generating correct and diverse solutions.