GenAI4ELab/papercli-papers huggingface dataset:
Don’t Be So Sure! Boosting ASR Decoding via Confidence Relaxation
Authors: Tomer Wullach; Shlomo E. Chazan
Venue: AAAI 2023 (main)

Abstract: Automatic Speech Recognition (ASR) systems frequently use a search-based decoding strategy aiming to find the best attainable transcript by considering multiple candidates. One prominent speech recognition decoding heuristic is beam search, which seeks the transcript with the greatest likelihood computed using the predicted distribution. While showing substantial performance gains in various tasks, beam search loses some of its effectiveness when the predicted probabilities are highly confident, i.e., the predicted distribution is massed for a single or very few classes. We show that recently proposed Self-Supervised Learning (SSL)-based ASR models tend to yield exceptionally confident predictions that may hamper beam search from truly considering a diverse set of candidates. We perform a layer analysis to reveal and visualize how predictions evolve, and propose a decoding procedure that improves the performance of fine-tuned ASR models. Our proposed approach does not require further training beyond the original fine-tuning, nor additional model parameters. In fact, we find that our proposed method requires significantly less inference computation than current approaches. We propose aggregating the top M layers, potentially leveraging useful information encoded in intermediate layers, and relaxing model confidence. We demonstrate the effectiveness of our approach by conducting an empirical study on varying amounts of labeled resources and different model sizes, showing consistent improvements in particular when applied to low-resource scenarios.
AMOM: Adaptive Masking over Masking for Conditional Masked Language Model
Authors: Yisheng Xiao; Ruiyang Xu; Lijun Wu; Juntao Li; Tao Qin; Tie-Yan Liu; Min Zhang
Venue: AAAI 2023 (main)

Abstract: Transformer-based autoregressive (AR) methods have achieved appealing performance for varied sequence-to-sequence generation tasks, e.g., neural machine translation, summarization, and code generation, but suffer from low inference efficiency. To speed up the inference stage, many non-autoregressive (NAR) strategies have been proposed in the past few years. Among them, the conditional masked language model (CMLM) is one of the most versatile frameworks, as it can support many different sequence generation scenarios and achieve very competitive performance on these tasks. In this paper, we further introduce a simple yet effective adaptive masking over masking strategy to enhance the refinement capability of the decoder and make the encoder optimization easier. Experiments on 3 different tasks (neural machine translation, summarization, and code generation) with 15 datasets in total confirm that our proposed simple method achieves significant performance improvement over the strong CMLM model. Surprisingly, our proposed model yields state-of-the-art performance on neural machine translation (34.62 BLEU on WMT16 EN to RO, 34.82 BLEU on WMT16 RO to EN, and 34.84 BLEU on IWSLT De to En) and even better performance than the AR Transformer on 7 benchmark datasets with at least 2.2x speedup. Our code is available at GitHub.
Global Mixup: Eliminating Ambiguity with Clustering
Authors: Xiangjin Xie; Li Yangning; Wang Chen; Kai Ouyang; Zuotong Xie; Hai-Tao Zheng
Venue: AAAI 2023 (main)

Abstract: Data augmentation with Mixup has been proven an effective method to regularize the current deep neural networks. Mixup generates virtual samples and corresponding labels simultaneously by linear interpolation. However, the one-stage generation paradigm and the use of linear interpolation have two defects: (1) The label of the generated sample is simply combined from the labels of the original sample pairs without reasonable judgment, resulting in ambiguous labels. (2) Linear combination significantly restricts the sampling space for generating samples. To address these issues, we propose a novel and effective augmentation method, Global Mixup, based on global clustering relationships.
Specifically, we transform the previous one-stage augmentation process into two-stage by decoupling the process of generating virtual samples from the labeling. And for the labels of the generated samples, relabeling is performed based on clustering by calculating the global relationships of the generated samples.
Furthermore, we are no longer restricted to linear relationships, which allows us to generate more reliable virtual samples in a larger sampling space.
Extensive experiments for CNN, LSTM, and BERT on five tasks show that Global Mixup outperforms previous baselines. Further experiments also demonstrate the advantage of Global Mixup in low-resource scenarios.
MoEC: Mixture of Expert Clusters
Authors: Yuan Xie; Shaohan Huang; Tianyu Chen; Furu Wei
Venue: AAAI 2023 (main)

Abstract: Sparsely Mixture of Experts (MoE) has received great interest due to its promising scaling capability with affordable computational overhead. MoE models convert dense layers into sparse experts, and utilize a gated routing network to make experts conditionally activated. However, as the number of experts grows, MoE with outrageous parameters suffers from overfitting and sparse data allocation. Such problems are especially severe on tasks with limited data, thus hindering the progress towards improving performance by scaling up. We verify that there exists a performance upper bound of scaling up sparse MoE. In this work, we propose Mixture of Expert Clusters — a general approach to enable expert layers to learn more diverse and appropriate knowledge by imposing variance-based constraints on the routing stage. Given this, we could further propose a cluster-level expert dropout strategy specifically designed for the expert cluster structure. Our experiments reveal that MoEC could improve performance on machine translation and natural language understanding tasks. MoEC plays a positive role in mitigating overfitting and sparse data allocation problems, thus fully releasing the potential of large-scale sparse models.
Factual and Informative Review Generation for Explainable Recommendation
Authors: Zhouhang Xie; Sameer Singh; Julian McAuley; Bodhisattwa Prasad Majumder
Venue: AAAI 2023 (main)

Abstract: Recent models can generate fluent and grammatical synthetic reviews while accurately predicting user ratings. The generated reviews, expressing users' estimated opinions towards related products, are often viewed as natural language ‘rationales’ for the jointly predicted rating. However, previous studies found that existing models often generate repetitive, universally applicable, and generic explanations, resulting in uninformative rationales. Further, our analysis shows that previous models' generated content often contain factual hallucinations. These issues call for novel solutions that could generate both informative and factually grounded explanations. Inspired by recent success in using retrieved content in addition to parametric knowledge for generation, we propose to augment the generator with a personalized retriever, where the retriever's output serves as external knowledge for enhancing the generator. Experiments on Yelp, TripAdvisor, and Amazon Movie Reviews dataset show our model could generate explanations that more reliably entail existing reviews, are more diverse, and are rated more informative by human evaluators.
Dialogue Rewriting via Skeleton-Guided Generation
Authors: Chunlei Xin; Hongyu Lin; Shan Wu; Xianpei Han; Bo Chen; Wen Dai; Shuai Chen; Bin Wang; Le Sun
Venue: AAAI 2023 (main)

Abstract: Dialogue rewriting aims to transform multi-turn, context-dependent dialogues into well-formed, context-independent text for most NLP systems. Previous dialogue rewriting benchmarks and systems assume a fluent and informative utterance to rewrite. Unfortunately, dialogue utterances from real-world systems are frequently noisy and with various kinds of errors that can make them almost uninformative. In this paper, we first present Real-world Dialogue Rewriting Corpus (RealDia), a new benchmark to evaluate how well current dialogue rewriting systems can deal with real-world noisy and uninformative dialogue utterances. RealDia contains annotated multi-turn dialogues from real scenes with ASR errors, spelling errors, redundancies and other noises that are ignored by previous dialogue rewriting benchmarks. We show that previous dialogue rewriting approaches are neither effective nor data-efficient to resolve RealDia. Then this paper presents Skeleton-Guided Rewriter (SGR), which can resolve the task of dialogue rewriting via a skeleton-guided generation paradigm. Experiments show that RealDia is a much more challenging benchmark for real-world dialogue rewriting, and SGR can effectively resolve the task and outperform previous approaches by a large margin.
Dialogue State Distillation Network with Inter-slot Contrastive Learning for Dialogue State Tracking
Authors: Jing Xu; Dandan Song; Chong Liu; Siu Cheung Hui; Fei Li; Qiang Ju; Xiaonan He; Jian Xie
Venue: AAAI 2023 (main)

Abstract: In task-oriented dialogue systems, Dialogue State Tracking (DST) aims to extract users' intentions from the dialogue history. Currently, most existing approaches suffer from error propagation and are unable to dynamically select relevant information when utilizing previous dialogue states. Moreover, the relations between the updates of different slots provide vital clues for DST. However, the existing approaches rely only on predefined graphs to indirectly capture the relations. In this paper, we propose a Dialogue State Distillation Network (DSDN) to utilize relevant information of previous dialogue states and migrate the gap of utilization between training and testing. Thus, it can dynamically exploit previous dialogue states and avoid introducing error propagation simultaneously. Further, we propose an inter-slot contrastive learning loss to effectively capture the slot co-update relations from dialogue context. Experiments are conducted on the widely used MultiWOZ 2.0 and MultiWOZ 2.1 datasets. The experimental results show that our proposed model achieves the state-of-the-art performance for DST.
Balanced Meta Learning and Diverse Sampling for Lifelong Task-Oriented Dialogue Systems
Authors: Qiancheng Xu; Min Yang; Ruifeng Xu
Venue: AAAI 2023 (main)

Abstract: In real-world scenarios, it is crucial to build a lifelong taskoriented dialogue system (TDS) that continually adapts to new knowledge without forgetting previously acquired experiences. Existing approaches mainly focus on mitigating the catastrophic forgetting in lifelong TDS. However, the transfer ability to generalize the accumulated old knowledge to new tasks is underexplored. In this paper, we propose a two-stage lifelong task-oriented dialogue generation method to mitigate catastrophic forgetting and encourage knowledge transfer simultaneously, inspired by the learning process. In the first stage, we learn task-specific masks which adaptively preserve the knowledge of each visited task so as to mitigate catastrophic forgetting. In this stage, we are expected to learn the task-specific knowledge which is tailored for each task. In the second stage, we bring the knowledge from the encountered tasks together and understand thoroughly. To this end, we devise a balanced meta learning strategy for both forward and backward knowledge transfer in the lifelong learning process. In particular, we perform meta-update with a meta-test set sampled from the current training data for forward knowledge transfer. In addition, we employ an uncertainty-based sampling strategy to select and store representative dialogue samples into episodic memory and perform meta-update with a meta-test set sampled from the memory for backward knowledge transfer. With extensive experiments on 29 tasks, we show that MetaLTDS outperforms the strong baselines in terms of both effectiveness and efficiency. For reproducibility, we submit our code at: https: //github.com/travis-xu/MetaLTDS.
Selector-Enhancer: Learning Dynamic Selection of Local and Non-local Attention Operation for Speech Enhancement
Authors: Xinmeng Xu; Weiping Tu; Yuhong Yang
Venue: AAAI 2023 (main)

Abstract: Attention mechanisms, such as local and non-local attention, play a fundamental role in recent deep learning based speech enhancement (SE) systems. However, a natural speech contains many fast-changing and relatively briefly acoustic events, therefore, capturing the most informative speech features by indiscriminately using local and non-local attention is challenged. We observe that the noise type and speech feature vary within a sequence of speech and the local and non-local can respectively process different types of corrupted speech regions. To leverage this, we propose Selector-Enhancer, a dual-attention based convolution neural network (CNN) with a feature-filter that can dynamically select regions from low-resolution speech features and feed them to local or non-local attention operations. In particular, the proposed feature-filter is trained by using reinforcement learning (RL) with a developed difficulty-regulated reward that related to network performance, model complexity and “the difficulty of the SE task”. The results show that our method achieves comparable or superior performance to existing approaches. In particular, Selector-Enhancer is effective for real-world denoising, where the number and types of noise are varies on a single noisy mixture.
A Graph Fusion Approach for Cross-Lingual Machine Reading Comprehension
Authors: Zenan Xu; Linjun Shou; Jian Pei; Ming Gong; Qinliang Su; Xiaojun Quan; Daxin Jiang
Venue: AAAI 2023 (main)

Abstract: Although great progress has been made for Machine Reading Comprehension (MRC) in English, scaling out to a large number of languages remains a huge challenge due to the lack of large amounts of annotated training data in non-English languages. To address this challenge, some recent efforts of cross-lingual MRC employ machine translation to transfer knowledge from English to other languages, through either explicit alignment or implicit attention. For effective knowledge transition, it is beneficial to leverage both semantic and syntactic information. However, the existing methods fail to explicitly incorporate syntax information in model learning. Consequently, the models are not robust to errors in alignment and noises in attention. In this work, we propose a novel approach, which jointly models the cross-lingual alignment information and the mono-lingual syntax information using a graph. We develop a series of algorithms, including graph construction, learning, and pre-training. The experiments on two benchmark datasets for cross-lingual MRC show that our approach outperforms all strong baselines, which verifies the effectiveness of syntax information for cross-lingual MRC.
Improving Biomedical Entity Linking with Cross-Entity Interaction
Authors: Zhenran Xu; Yulin Chen; Baotian Hu
Venue: AAAI 2023 (main)

Abstract: Biomedical entity linking (EL) is the task of linking mentions in a biomedical document to corresponding entities in a knowledge base (KB). The challenge in biomedical EL lies in leveraging mention context to select the most appropriate entity among possible candidates. Although some EL models achieve competitive results by retrieving candidate entities and then exploiting context to re-rank them, these re-ranking models concatenate mention context with one candidate at a time. They lack fine-grained interaction among candidates, and potentially cannot handle ambiguous mentions when facing candidates both with high lexical similarity. We cope with this issue using a re-ranking model based on prompt tuning, which represents mention context and all candidates at once, letting candidates in comparison attend to each other. We also propose a KB-enhanced self-supervised pretraining strategy. Instead of large-scale pretraining on biomedical EL data in previous work, we use masked language modeling with synonyms from KB. Our method achieves state-of-the-art results on 3 biomedical EL datasets: NCBI disease, BC5CDR and COMETA, showing the effectiveness of cross-entity interaction and KB-enhanced pretraining strategy. Code is available at https://github.com/HITsz-TMG/Prompt-BioEL.
Nested Named Entity Recognition as Building Local Hypergraphs
Authors: Yukun Yan; Bingling Cai; Sen Song
Venue: AAAI 2023 (main)

Abstract: Named entity recognition is a fundamental task in natural language processing. Based on the sequence labeling paradigm for flat named entity recognition, multiple methods have been developed to handle the nested structures. However, they either require fixed recognition order or introduce complex hypergraphs. To tackle this problem, we propose a novel model named Local Hypergraph Builder Network (LHBN) that builds multiple simpler local hypergraphs to capture named entities instead of a single complex full-size hypergraph. The proposed model has three main properties: (1) The named entities that share boundaries are captured in the same local hypergraph. (2) The boundary information is enhanced by building local hypergraphs. (3) The hypergraphs can be built bidirectionally to take advantage of the identification direction preference of different named entities. Experiments illustrate that our model outperforms previous state-of-the-art methods on four widely used nested named entity recognition datasets: ACE04, ACE05, GENIA, and KBP17. The code is available at https://github.com/yanyk13/local-hypergraph-building-network.git.
A Domain-Transfer Meta Task Design Paradigm for Few-Shot Slot Tagging
Authors: Fengyi Yang; Xi Zhou; Yating Yang; Bo Ma; Rui Dong; Abibulla Atawulla
Venue: AAAI 2023 (main)

Abstract: Few-shot slot tagging is an important task in dialogue systems and attracts much attention of researchers. Most previous few-shot slot tagging methods utilize meta-learning procedure for training and strive to construct a large number of different meta tasks to simulate the testing situation of insufficient data. However, there is a widespread phenomenon of overlap slot between two domains in slot tagging. Traditional meta tasks ignore this special phenomenon and cannot simulate such realistic few-shot slot tagging scenarios. It violates the basic principle of meta-learning which the meta task is consistent with the real testing task, leading to historical information forgetting problem. In this paper, we introduce a novel domain-transfer meta task design paradigm to tackle this problem. We distribute a basic domain to each target domain based on the coincidence degree of slot labels between these two domains. Unlike classic meta tasks which only rely on small samples of target domain, our meta tasks aim to correctly infer the class of target domain query samples based on both abundant data in basic domain and scarce data in target domain. To accomplish our meta task, we propose a Task Adaptation Network to effectively transfer the historical information from the basic domain to the target domain. We carry out sufficient experiments on the benchmark slot tagging dataset SNIPS and the name entity recognition dataset NER. Results demonstrate that our proposed model outperforms previous methods and achieves the state-of-the-art performance.
Orders Are Unwanted: Dynamic Deep Graph Convolutional Network for Personality Detection
Authors: Tao Yang; Jinghao Deng; Xiaojun Quan; Qifan Wang
Venue: AAAI 2023 (main)

Abstract: Predicting personality traits based on online posts has emerged as an important task in many fields such as social network analysis. One of the challenges of this task is assembling information from various posts into an overall profile for each user. While many previous solutions simply concatenate the posts into a long text and then encode the text by sequential or hierarchical models, they introduce unwarranted orders for the posts, which may mislead the models. In this paper, we propose a dynamic deep graph convolutional network (D-DGCN) to overcome the above limitation. Specifically, we design a learn-to-connect approach that adopts a dynamic multi-hop structure instead of a deterministic structure, and combine it with the DGCN module to automatically learn the connections between posts. The modules of post encoder, learn-to-connect, and DGCN are jointly trained in an end-to-end manner. Experimental results on the Kaggle and Pandora datasets show the superior performance of D-DGCN to state-of-the-art baselines. Our code is available at https://github.com/djz233/D-DGCN.
What Does Your Face Sound Like? 3D Face Shape towards Voice
Authors: Zhihan Yang; Zhiyong Wu; Ying Shan; Jia Jia
Venue: AAAI 2023 (main)

Abstract: Face-based speech synthesis provides a practical solution to generate voices from human faces. However, directly using 2D face images leads to the problems of uninterpretability and entanglement. In this paper, to address the issues, we introduce 3D face shape which (1) has an anatomical relationship between voice characteristics, partaking in the "bone conduction" of human timbre production, and (2) is naturally independent of irrelevant factors by excluding the blending process. We devise a three-stage framework to generate speech from 3D face shapes. Fully considering timbre production in anatomical and acquired terms, our framework incorporates three additional relevant attributes including face texture, facial features, and demographics. Experiments and subjective tests demonstrate our method can generate utterances matching faces well, with good audio quality and voice diversity. We also explore and visualize how the voice changes with the face. Case studies show that our method upgrades the face-voice inference to personalized custom-made voice creating, revealing a promising prospect in virtual human and dubbing applications.
FiTs: Fine-Grained Two-Stage Training for Knowledge-Aware Question Answering
Authors: Qichen Ye; Bowen Cao; Nuo Chen; Weiyuan Xu; Yuexian Zou
Venue: AAAI 2023 (main)

Abstract: Knowledge-aware question answering (KAQA) requires the model to answer questions over a knowledge base, which is essential for both open-domain QA and domain-specific QA, especially when language models alone cannot provide all the knowledge needed.
Despite the promising result of recent KAQA systems which tend to integrate linguistic knowledge from pre-trained language models (PLM) and factual knowledge from knowledge graphs (KG) to answer complex questions, a bottleneck exists in effectively fusing the representations from PLMs and KGs because of (i) the semantic and distributional gaps between them, and (ii) the difficulties in joint reasoning over the provided knowledge from both modalities.
To address the above two problems, we propose a Fine-grained Two-stage training framework (FiTs) to boost the KAQA system performance: The first stage aims at aligning representations from the PLM and the KG, thus bridging the modality gaps between them, named knowledge adaptive post-training. The second stage, called knowledge-aware fine-tuning, aims to improve the model's joint reasoning ability based on the aligned representations.
In detail,  we fine-tune the post-trained model via two auxiliary self-supervised tasks in addition to the QA supervision.
Extensive experiments demonstrate that our approach achieves state-of-the-art performance on three benchmarks in the commonsense reasoning (i.e., CommonsenseQA, OpenbookQA) and medical question answering (i.e., MedQA-USMILE) domains.