GenAI4ELab/papercli-papers huggingface dataset:
Converge to the Truth: Factual Error Correction via Iterative Constrained Editing
Authors: Jiangjie Chen; Rui Xu; Wenxuan Zeng; Changzhi Sun; Lei Li; Yanghua Xiao
Venue: AAAI 2023 (main)

Abstract: Given a possibly false claim sentence, how can we automatically correct it with minimal editing? Existing methods either require a large number of pairs of false and corrected claims for supervised training or do not handle well errors spanning over multiple tokens within an utterance. In this paper, we propose VENCE, a novel method for factual error correction (FEC) with minimal edits. VENCE formulates the FEC problem as iterative sampling editing actions with respect to a target density function. We carefully design the target function with predicted truthfulness scores from an offline trained fact verification model. VENCE samples the most probable editing positions based on back-calculated gradients of the truthfulness score concerning input tokens and the editing actions using a distantly-supervised language model (T5). Experiments on a public dataset show that VENCE improves the well-adopted SARI metric by 5.3 (or a relative improvement of 11.8%) over the previous best distantly-supervised methods.
Adversarial Word Dilution as Text Data Augmentation in Low-Resource Regime
Authors: Junfan Chen; Richong Zhang; Zheyan Luo; Chunming Hu; Yongyi Mao
Venue: AAAI 2023 (main)

Abstract: Data augmentation is widely used in text classification, especially in the low-resource regime where a few examples for each class are available during training. Despite the success, generating data augmentations as hard positive examples that may increase their effectiveness is under-explored. This paper proposes an Adversarial Word Dilution (AWD) method that can generate hard positive examples as text data augmentations to train the low-resource text classification model efficiently. Our idea of augmenting the text data is to dilute the embedding of strong positive words by weighted mixing with unknown-word embedding, making the augmented inputs hard to be recognized as positive by the classification model. We adversarially learn the dilution weights through a constrained min-max optimization process with the guidance of the labels. Empirical studies on three benchmark datasets show that AWD can generate more effective data augmentations and outperform the state-of-the-art text data augmentation methods. The additional analysis demonstrates that the data augmentations generated by AWD are interpretable and can flexibly extend to new examples without further training.
CP-Rec: Contextual Prompting for Conversational Recommender Systems
Authors: Keyu Chen; Shiliang Sun
Venue: AAAI 2023 (main)

Abstract: The conversational recommender system (CRS) aims to provide high-quality recommendations through interactive dialogues. However, previous CRS models have no effective mechanisms for task planning and topic elaboration, and thus they hardly maintain coherence in multi-task recommendation dialogues. Inspired by recent advances in prompt-based learning, we propose a novel contextual prompting framework for dialogue management, which optimizes prompts based on context, topics, and user profiles. Specifically, we develop a topic controller to sequentially plan the subtasks, and a prompt search module to construct context-aware prompts. We further adopt external knowledge to enrich user profiles and make knowledge-aware recommendations. Incorporating these techniques, we propose a conversational recommender system with contextual prompting, namely CP-Rec. Experimental results demonstrate that it achieves state-of-the-art recommendation accuracy and generates more coherent and informative conversations.
A Vector Quantized Approach for Text to Speech Synthesis on Real-World Spontaneous Speech
Authors: Li-Wei Chen; Shinji Watanabe; Alexander Rudnicky
Venue: AAAI 2023 (main)

Abstract: Recent Text-to-Speech (TTS) systems trained on reading or acted corpora have achieved near human-level naturalness. The diversity of human speech, however, often goes beyond the coverage of these corpora. We believe the ability to handle such diversity is crucial for AI systems to achieve human-level communication. Our work explores the use of more abundant real-world data for building speech synthesizers. We train TTS systems using real-world speech from YouTube and podcasts. We observe the mismatch between training and inference alignments in mel-spectrogram based autoregressive models, leading to unintelligible synthesis, and demonstrate that learned discrete codes within multiple code groups effectively resolves this issue. We introduce our MQTTS system whose architecture is designed for multiple code generation and monotonic alignment, along with the use of a clean silence prompt to improve synthesis quality. We conduct ablation analyses to identify the efficacy of our methods. We show that MQTTS outperforms existing TTS systems in several objective and subjective measures.
Learning to Memorize Entailment and Discourse Relations for Persona-Consistent Dialogues
Authors: Ruijun Chen; Jin Wang; Liang-Chih Yu; Xuejie Zhang
Venue: AAAI 2023 (main)

Abstract: Maintaining engagement and consistency is particularly important in dialogue systems. Existing works have improved the performance of dialogue systems by intentionally learning interlocutor personas with sophisticated network structures. One issue with this approach is that it requires more personal corpora with annotations. Additionally, these models typically perform the next utterance prediction to generate a response but neglect the discourse coherence in the entire conversation. To address these issues, this study proposes a method of learning to memorize entailment and discourse relations for persona-consistent dialogue tasks. Entailment text pairs in natural language inference dataset were applied to learn latent entailment relations as external memories by premise-to-hypothesis generation task. Furthermore, an internal memory with a similar architecture was applied to the discourse information in the dialogue. Placing orthogonality restrictions on these two memory spaces ensures that the latent entailment relations remain dialogue-independent. Both memories collaborate to obtain entailment and discourse representation for the generation, allowing a deeper understanding of both consistency and coherence. Experiments on two large public datasets, PersonaChat and DSTC7-AVSD, demonstrated the effectiveness of the proposed method. Both automatic and human evaluations indicate that the proposed model outperforms several strong baselines in terms of both persona consistency and response coherence. Our source code is availabled at https://github.com/Chenrj233/LMEDR.
Preference-Controlled Multi-Objective Reinforcement Learning for Conditional Text Generation
Authors: Wenqing Chen; Jidong Tian; Caoyun Fan; Yitian Li; Hao He; Yaohui Jin
Venue: AAAI 2023 (main)

Abstract: Conditional text generation is to generate text sequences conditioning on linguistic or non-linguistic data. The main line of existing work proposed deterministic models to improve the fidelity of the generated text but often ignored the diversity. Another line relied on conditional variational auto-encoders (CVAEs), which increased the diversity over their deterministic backbones. However, CVAEs regard diversity as an implicit objective and may not be optimal. In this paper, we raise two questions:  i) Can diversity be further improved with an explicit objective? ii) Since fidelity and diversity are two conflicting objectives, how can we obtain different multi-objective optimal solutions according to user preferences? To answer question i), we propose a multi-objective reinforcement learning (MORL) method which explicitly takes CIDEr and Self-CIDEr scores as the fidelity-oriented and diversity-oriented rewards respectively. To answer question ii), we propose a preference-controlled MORL method, which can obtain infinite multi-objective optimal solutions by tuning the preference variable. We conduct extensive experiments on paraphrasing and image captioning tasks, which show that in the fidelity-diversity trade-off space, our model outperforms both deterministic and CVAE-based baselines.
Learning towards Selective Data Augmentation for Dialogue Generation
Authors: Xiuying Chen; Mingzhe Li; Jiayi Zhang; Xiaoqiang Xia; Chen Wei; Jianwei Cui; Xin Gao; Xiangliang Zhang; Rui Yan
Venue: AAAI 2023 (main)

Abstract: As it is cumbersome and expensive to acquire a huge amount of data for training neural dialog models, data augmentation is proposed to effectively utilize existing training samples.
However, current data augmentation techniques on the dialog generation task mostly augment all cases in the training dataset without considering the intrinsic attributes between different cases.
We argue that not all cases are beneficial for augmentation task, and the cases suitable for augmentation should obey the following two attributes: 
(1) low-quality (the dialog model cannot generate a high-quality response for the case),
(2) representative (the case should represent the property of the whole dataset).
Herein, we explore this idea by proposing a Selective Data Augmentation framework (SDA) for the response generation task.
SDA employs a dual adversarial network to select the lowest quality and most representative data points for augmentation in one stage. 
Extensive experiments conducted on two publicly available datasets, i.e., DailyDialog and OpenSubtitles, show that our framework can improve the response generation performance with respect to various metrics
Learn from Yesterday: A Semi-supervised Continual Learning Method for Supervision-Limited Text-to-SQL Task Streams
Authors: Yongrui Chen; Xinnan Guo; Tongtong Wu; Guilin Qi; Yang Li; Yang Dong
Venue: AAAI 2023 (main)

Abstract: Conventional text-to-SQL studies are limited to a single task with a fixed-size training and test set. When confronted with a stream of tasks common in real-world applications, existing methods struggle with the problems of insufficient supervised data and high retraining costs. The former tends to cause overfitting on unseen databases for the new task, while the latter makes a full review of instances from past tasks impractical for the model, resulting in forgetting of learned SQL structures and database schemas. To address the problems, this paper proposes integrating semi-supervised learning (SSL) and continual learning (CL) in a stream of text-to-SQL tasks and offers two promising solutions in turn. The first solution Vanilla is to perform self-training, augmenting the supervised training data with predicted pseudo-labeled instances of the current task, while replacing the full volume retraining with episodic memory replay to balance the training efficiency with the performance of previous tasks. The improved solution SFNet takes advantage of the intrinsic connection between CL and SSL. It uses in-memory past information to help current SSL, while adding high-quality pseudo instances in memory to improve future replay.  The experiments on two datasets shows that SFNet outperforms the widely-used SSL-only and CL-only baselines on multiple metrics.
A Scope Sensitive and Result Attentive Model for Multi-Intent Spoken Language Understanding
Authors: Lizhi Cheng; Wenmian Yang; Weijia Jia
Venue: AAAI 2023 (main)

Abstract: Multi-Intent Spoken Language Understanding (SLU), a novel and more complex scenario of SLU, is attracting increasing attention. Unlike traditional SLU, each intent in this scenario has its specific scope. Semantic information outside the scope even hinders the prediction, which tremendously increases the difficulty of intent detection. More seriously, guiding slot filling with these inaccurate intent labels suffers error propagation problems, resulting in unsatisfied overall performance. To solve these challenges, in this paper, we propose a novel Scope-Sensitive Result Attention Network (SSRAN) based on Transformer, which contains a Scope Recognizer (SR) and a Result Attention Network (RAN). SR assignments scope information to each token, reducing the distraction of out-of-scope tokens. RAN effectively utilizes the bidirectional interaction between SF and ID results, mitigating the error propagation problem. Experiments on two public datasets indicate that our model significantly improves SLU performance (5.4% and 2.1% on Overall accuracy) over the state-of-the-art baseline.
Unsupervised Explanation Generation via Correct Instantiations
Authors: Sijie Cheng; Zhiyong Wu; Jiangjie Chen; Zhixing Li; Yang Liu; Lingpeng Kong
Venue: AAAI 2023 (main)

Abstract: While large pre-trained language models (PLM) have shown their great skills at solving discriminative tasks, a significant gap remains when compared with humans for explanation-related tasks. Among them, explaining the reason why a statement is wrong (e.g., against commonsense) is incredibly challenging. 
The major difficulty is finding the conflict point, where the statement contradicts our real world. This paper proposes Neon, a two-phrase, unsupervised explanation generation framework. Neon first generates corrected instantiations of the statement (phase I), then uses them to prompt large PLMs to find the conflict point and complete the explanation (phase II). We conduct extensive experiments on two standard explanation benchmarks, i.e., ComVE and e-SNLI. According to both automatic and human evaluations, Neon outperforms baselines, even for those with human-annotated instantiations. In addition to explaining a negative prediction, we further demonstrate that Neon remains effective when generalizing to different scenarios. The resources of Neon are available at: https://github.com/Shark-NLP/Neon.
Prompt-Augmented Linear Probing: Scaling beyond the Limit of Few-Shot In-Context Learners
Authors: Hyunsoo Cho; Hyuhng Joon Kim; Junyeob Kim; Sang-Woo Lee; Sang-goo Lee; Kang Min Yoo; Taeuk Kim
Venue: AAAI 2023 (main)

Abstract: Through in-context learning (ICL), large-scale language models are effective few-shot learners without additional model fine-tuning.  However, the ICL performance does not scale well with the number of available training sample as it is limited by the inherent input length constraint of the underlying language model. Meanwhile, many studies have revealed that language models are also powerful feature extractors, allowing them to be utilized in a black-box manner and enabling the linear probing paradigm, where lightweight discriminators are trained on top of the pre-extracted input representations. This paper proposes prompt-augmented linear probing (PALP), a hybrid of linear probing and ICL, which leverages the best of both worlds. PALP inherits the scalability of linear probing and the capability of enforcing language models to derive more meaningful representations via tailoring input into a more conceivable form. Throughout in-depth investigations on various datasets, we verified that PALP significantly closes the gap between ICL in the data-hungry scenario and fine-tuning in the data-abundant scenario with little training overhead, potentially making PALP a strong alternative in a black-box scenario.
Neural Dynamic Focused Topic Model
Authors: Kostadin Cvejoski; Ramsés J. Sánchez; César Ojeda
Venue: AAAI 2023 (main)

Abstract: Topic models and all their variants analyse text by learning meaningful representations through word co-occurrences. As pointed out by previous work, such models implicitly assume that the probability of a topic to be active and its proportion within each document are positively correlated. This correlation can be strongly detrimental in the case of documents created over time, simply because recent documents are likely better described by new and hence rare topics. In this work we leverage recent advances in neural variational inference and present an alternative neural approach to the dynamic Focused Topic Model. Indeed, we develop a neural model for topic evolution which exploits sequences of Bernoulli random variables in order to track the appearances of topics, thereby decoupling their activities from their proportions. We evaluate our model on three different datasets (the UN general debates, the collection of NeurIPS papers, and the ACL Anthology dataset) and show that it (i) outperforms state-of-the-art topic models in generalization tasks and (ii) performs comparably to them on prediction tasks, while employing roughly the same number of parameters, and converging about two times faster.
Improving Simultaneous Machine Translation with Monolingual Data
Authors: Hexuan Deng; Liang Ding; Xuebo Liu; Meishan Zhang; Dacheng Tao; Min Zhang
Venue: AAAI 2023 (main)

Abstract: Simultaneous machine translation (SiMT) is usually done via sequence-level knowledge distillation (Seq-KD) from a full-sentence neural machine translation (NMT) model. However, there is still a significant performance gap between NMT and SiMT. In this work, we propose to leverage monolingual data to improve SiMT, which trains a SiMT student on the combination of bilingual data and external monolingual data distilled by Seq-KD. Preliminary experiments on En-Zh and En-Ja news domain corpora demonstrate that monolingual data can significantly improve translation quality (e.g., +3.15 BLEU on En-Zh). Inspired by the behavior of human simultaneous interpreters, we propose a novel monolingual sampling strategy for SiMT, considering both chunk length and monotonicity. Experimental results show that our sampling strategy consistently outperforms the random sampling strategy (and other conventional typical NMT monolingual sampling strategies) by avoiding the key problem of SiMT -- hallucination, and has better scalability. We achieve +0.72 BLEU improvements on average against random sampling on En-Zh and En-Ja. Data and codes can be found at https://github.com/hexuandeng/Mono4SiMT.
Domain-Adapted Dependency Parsing for Cross-Domain Named Entity Recognition
Authors: Chenxiao Dou; Xianghui Sun; Yaoshu Wang; Yunjie Ji; Baochang Ma; Xiangang Li
Venue: AAAI 2023 (main)

Abstract: In recent years, many researchers have leveraged structural information from dependency trees to improve Named Entity Recognition (NER). Most of their methods take dependency-tree labels as input features for NER model training. However, such dependency information is not inherently provided in most NER corpora, making the methods with low usability in practice. To effectively exploit the potential of word-dependency knowledge, motivated by the success of Multi-Task Learning on cross-domain NER, we investigate a novel NER learning method incorporating cross-domain Dependency Parsing (DP) as its auxiliary learning task. Then, considering the high consistency of word-dependency relations across domains, we present an unsupervised domain-adapted method to transfer word-dependency knowledge from high-resource domains to low-resource ones. With the help of cross-domain DP to bridge different domains, both useful cross-domain and cross-task knowledge can be learned by our model to considerably benefit cross-domain NER. To make better use of the cross-task knowledge between NER and DP, we unify both tasks in a shared network architecture for joint learning, using Maximum Mean Discrepancy(MMD). Finally, through extensive experiments, we show our proposed method can not only effectively take advantage of word-dependency knowledge, but also significantly outperform other Multi-Task Learning methods on cross-domain NER. Our code is open-source and available at https://github.com/xianghuisun/DADP.
MultiSpider: Towards Benchmarking Multilingual Text-to-SQL Semantic Parsing
Authors: Longxu Dou; Yan Gao; Mingyang Pan; Dingzirui Wang; Wanxiang Che; Dechen Zhan; Jian-Guang Lou
Venue: AAAI 2023 (main)

Abstract: Text-to-SQL semantic parsing is an important NLP task, which facilitates the interaction between users and the database. Much recent progress in text-to-SQL has been driven by large-scale datasets, but most of them are centered on English. In this work, we present MultiSpider, the largest multilingual text-to-SQL semantic parsing dataset which covers seven languages (English, German, French, Spanish, Japanese, Chinese, and Vietnamese). Upon MultiSpider we further identify the lexical and structural challenges of text-to-SQL  (caused by specific language properties and dialect sayings) and their intensity across different languages. Experimental results under various settings (zero-shot, monolingual and multilingual) reveal a 6.1% absolute drop in accuracy in non-English languages. Qualitative and quantitative analyses are conducted to understand the reason for the performance drop of each language. Besides the dataset, we also propose a simple schema augmentation framework SAVe (Schema-Augmentation-with-Verification), which significantly boosts the overall performance by about 1.8% and closes the 29.5% performance gap across languages.
Learning to Select from Multiple Options
Authors: Jiangshu Du; Wenpeng Yin; Congying Xia; Philip S. Yu
Venue: AAAI 2023 (main)

Abstract: Many NLP tasks can be regarded as a selection problem from a set of options, such as classification tasks, multi-choice question answering, etc. Textual entailment (TE) has been shown as the state-of-the-art (SOTA) approach to dealing with those selection problems. TE treats input texts as premises (P), options as hypotheses (H), then handles the selection problem by modeling (P, H) pairwise. Two limitations: first, the pairwise modeling is unaware of other options, which is less intuitive since humans often determine the best options by comparing competing candidates; second, the inference process of pairwise TE is time-consuming, especially when the option space is large. To deal with the two issues, this work first proposes a contextualized TE model (Context-TE) by appending other k options as the context of the current (P, H) modeling. Context-TE is able to learn more reliable decision for the H since it considers various context. Second, we speed up Context-TE by coming up with Parallel-TE, which learns the decisions of multiple options simultaneously. Parallel-TE significantly improves the inference speed while keeping comparable performance with Context-TE. Our methods are evaluated on three tasks (ultra-fine entity typing, intent detection and multi-choice QA) that are typical selection problems with different sizes of options. Experiments show our models set new SOTA performance; particularly, Parallel-TE is faster than the pairwise TE by k times in inference.
Real or Fake Text?: Investigating Human Ability to Detect Boundaries between Human-Written and Machine-Generated Text
Authors: Liam Dugan; Daphne Ippolito; Arun Kirubarajan; Sherry Shi; Chris Callison-Burch
Venue: AAAI 2023 (main)

Abstract: As text generated by large language models proliferates, it becomes vital to understand how humans engage with such text, and whether or not they are able to detect when the text they are reading did not originate with a human writer. Prior work on human detection of generated text focuses on the case where an entire passage is either human-written or machine-generated. In this paper, we study a more realistic setting where text begins as human-written and transitions to being generated by state-of-the-art neural language models. We show that, while annotators often struggle at this task, there is substantial variance in annotator skill and that given proper incentives, annotators can improve at this task over time. Furthermore, we conduct a detailed comparison study and analyze how a variety of variables (model size, decoding strategy, fine-tuning, prompt genre, etc.) affect human detection performance. Finally, we collect error annotations from our participants and use them to show that certain textual genres influence models to make different types of errors and that certain sentence-level features correlate highly with annotator selection. We release the RoFT dataset: a collection of over 21,000 human annotations paired with error classifications to encourage future work in human detection and evaluation of generated text.