arXiv subjects

Mengyue Wu

Publications and source records attributed to Mengyue Wu.

At least 73 records · Page 4Linked to original sources

arXiv2023

Semantic Space Grounded Weighted Decoding for Multi-Attribute Controllable Dialogue Generation

Controlling chatbot utterance generation with multiple attributes such as personalities, emotions and dialogue acts is a practically useful but under-studied problem. We propose a novel framework called DASC that possesses strong controllability with a weighted decoding paradigm, while improving generation quality with the grounding in an attribute semantics space. Generation with multiple attributes is then intuitively implemented with an interpolation of multiple attribute embeddings, which results in substantial reduction in the model sizes. Experiments show that DASC can achieve high control accuracy in generation task with the simultaneous control of 3 aspects while also producing interesting and reasonably sensible responses, even in an out-of-distribution robustness test.

cs.CL↗

arXiv2023

Does My Dog ''Speak'' Like Me? The Acoustic Correlation between Pet Dogs and Their Human Owners

How hosts language influence their pets' vocalization is an interesting yet underexplored problem. This paper presents a preliminary investigation into the possible correlation between domestic dog vocal expressions and their human host's language environment. We first present a new dataset of Shiba Inu dog vocals from YouTube, which provides 7500 clean sound clips, including their contextual information of these vocals and their owner's speech clips with a carefully-designed data processing pipeline. The contextual information includes the scene category in which the vocal was recorded, the dog's location and activity. With a classification task and prominent factor analysis, we discover significant acoustic differences in the dog vocals from the two language environments. We further identify some acoustic features from dog vocalizations that are potentially correlated to their host language patterns.

cs.SD↗

arXiv2023

Towards Lexical Analysis of Dog Vocalizations via Online Videos

Deciphering the semantics of animal language has been a grand challenge. This study presents a data-driven investigation into the semantics of dog vocalizations via correlating different sound types with consistent semantics. We first present a new dataset of Shiba Inu sounds, along with contextual information such as location and activity, collected from YouTube with a well-constructed pipeline. The framework is also applicable to other animal species. Based on the analysis of conditioned probability between dog vocalizations and corresponding location and activity, we discover supporting evidence for previous heuristic research on the semantic meaning of various dog sounds. For instance, growls can signify interactions. Furthermore, our study yields new insights that existing word types can be subdivided into finer-grained subtypes and minimal semantic unit for Shiba Inu is word-related. For example, whimper can be subdivided into two types, attention-seeking and discomfort.

cs.SD↗

arXiv2023

Improving Audio Caption Fluency with Automatic Error Correction

Automated audio captioning (AAC) is an important cross-modality translation task, aiming at generating descriptions for audio clips. However, captions generated by previous AAC models have faced ``false-repetition'' errors due to the training objective. In such scenarios, we propose a new task of AAC error correction and hope to reduce such errors by post-processing AAC outputs. To tackle this problem, we use observation-based rules to corrupt captions without errors, for pseudo grammatically-erroneous sentence generation. One pair of corrupted and clean sentences can thus be used for training. We train a neural network-based model on the synthetic error dataset and apply the model to correct real errors in AAC outputs. Results on two benchmark datasets indicate that our approach significantly improves fluency while maintaining semantic information.

eess.AS↗

arXiv2023

LLM-empowered Chatbots for Psychiatrist and Patient Simulation: Application and Evaluation

Empowering chatbots in the field of mental health is receiving increasing amount of attention, while there still lacks exploration in developing and evaluating chatbots in psychiatric outpatient scenarios. In this work, we focus on exploring the potential of ChatGPT in powering chatbots for psychiatrist and patient simulation. We collaborate with psychiatrists to identify objectives and iteratively develop the dialogue system to closely align with real-world scenarios. In the evaluation experiments, we recruit real psychiatrists and patients to engage in diagnostic conversations with the chatbots, collecting their ratings for assessment. Our findings demonstrate the feasibility of using ChatGPT-powered chatbots in psychiatric scenarios and explore the impact of prompt designs on chatbot behavior and user experience.

cs.CL↗

arXiv2023

Diverse and Vivid Sound Generation from Text Descriptions

Previous audio generation mainly focuses on specified sound classes such as speech or music, whose form and content are greatly restricted. In this paper, we go beyond specific audio generation by using natural language description as a clue to generate broad sounds. Unlike visual information, a text description is concise by its nature but has rich hidden meanings beneath, which poses a higher possibility and complexity on the audio to be generated. A Variation-Quantized GAN is used to train a codebook learning discrete representations of spectrograms. For a given text description, its pre-trained embedding is fed to a Transformer to sample codebook indices to decode a spectrogram to be further transformed into waveform by a melgan vocoder. The generated waveform has high quality and fidelity while excellently corresponding to the given text. Experiments show that our proposed method is capable of generating natural, vivid audios, achieving superb quantitative and qualitative results.

cs.SD↗

arXiv2022

D4: a Chinese Dialogue Dataset for Depression-Diagnosis-Oriented Chat

In a depression-diagnosis-directed clinical session, doctors initiate a conversation with ample emotional support that guides the patients to expose their symptoms based on clinical diagnosis criteria. Such a dialogue system is distinguished from existing single-purpose human-machine dialog systems, as it combines task-oriented and chit-chats with uniqueness in dialogue topics and procedures. However, due to the social stigma associated with mental illness, the dialogue data related to depression consultation and diagnosis are rarely disclosed. Based on clinical depression diagnostic criteria ICD-11 and DSM-5, we designed a 3-phase procedure to construct D$^4$: a Chinese Dialogue Dataset for Depression-Diagnosis-Oriented Chat, which simulates the dialogue between doctors and patients during the diagnosis of depression, including diagnosis results and symptom summary given by professional psychiatrists for each conversation. Upon the newly-constructed dataset, four tasks mirroring the depression diagnosis process are established: response generation, topic prediction, dialog summary, and severity classification of depressive episode and suicide risk. Multi-scale evaluation results demonstrate that a more empathy-driven and diagnostic-accurate consultation dialogue system trained on our dataset can be achieved compared to rule-based bots.

cs.CL↗

arXiv2022

DFM: Dialogue Foundation Model for Universal Large-Scale Dialogue-Oriented Task Learning

Building a universal conversational agent has been a long-standing goal of the dialogue research community. Most previous works only focus on a small set of dialogue tasks. In this work, we aim to build a unified dialogue foundation model (DFM) which can be used to solve massive diverse dialogue tasks. To achieve this goal, a large-scale well-annotated dialogue dataset with rich task diversity (DialogZoo) is collected. We introduce a framework to unify all dialogue tasks and propose novel auxiliary self-supervised tasks to achieve stable training of DFM on the highly diverse large scale DialogZoo corpus. Experiments show that, compared with models of the same size, DFM can achieve state-of-the-art or competitive performance on very rich cross-domain downstream dialogue tasks. This demonstrates that DFM largely extends the ability of unified dialogue pre-trained model.

cs.CL↗

arXiv2022

OPAL: Ontology-Aware Pretrained Language Model for End-to-End Task-Oriented Dialogue

This paper presents an ontology-aware pretrained language model (OPAL) for end-to-end task-oriented dialogue (TOD). Unlike chit-chat dialogue models, task-oriented dialogue models fulfill at least two task-specific modules: dialogue state tracker (DST) and response generator (RG). The dialogue state consists of the domain-slot-value triples, which are regarded as the user's constraints to search the domain-related databases. The large-scale task-oriented dialogue data with the annotated structured dialogue state usually are inaccessible. It prevents the development of the pretrained language model for the task-oriented dialogue. We propose a simple yet effective pretraining method to alleviate this problem, which consists of two pretraining phases. The first phase is to pretrain on large-scale contextual text data, where the structured information of the text is extracted by the information extracting tool. To bridge the gap between the pretraining method and downstream tasks, we design two pretraining tasks: ontology-like triple recovery and next-text generation, which simulates the DST and RG, respectively. The second phase is to fine-tune the pretrained model on the TOD data. The experimental results show that our proposed method achieves an exciting boost and get competitive performance even without any TOD data on CamRest676 and MultiWOZ benchmarks.

cs.CL↗

arXiv2022

Symptom Identification for Interpretable Detection of Multiple Mental Disorders

Mental disease detection (MDD) from social media has suffered from poor generalizability and interpretability, due to lack of symptom modeling. This paper introduces PsySym, the first annotated symptom identification corpus of multiple psychiatric disorders, to facilitate further research progress. PsySym is annotated according to a knowledge graph of the 38 symptom classes related to 7 mental diseases complied from established clinical manuals and scales, and a novel annotation framework for diversity and quality. Experiments show that symptom-assisted MDD enabled by PsySym can outperform strong pure-text baselines. We also exhibit the convincing MDD explanations provided by symptom predictions with case studies, and point to their further potential applications.

cs.CL↗

arXiv2022

Psychiatric Scale Guided Risky Post Screening for Early Detection of Depression

Depression is a prominent health challenge to the world, and early risk detection (ERD) of depression from online posts can be a promising technique for combating the threat. Early depression detection faces the challenge of efficiently tackling streaming data, balancing the tradeoff between timeliness, accuracy and explainability. To tackle these challenges, we propose a psychiatric scale guided risky post screening method that can capture risky posts related to the dimensions defined in clinical depression scales, and providing interpretable diagnostic basis. A Hierarchical Attentional Network equipped with BERT (HAN-BERT) is proposed to further advance explainable predictions. For ERD, we propose an online algorithm based on an evolving queue of risky posts that can significantly reduce the number of model inferences to boost efficiency. Experiments show that our method outperforms the competitive feature-based and neural models under conventional depression detection settings, and achieves simultaneous improvement in both efficacy and efficiency for ERD.

cs.CL↗

arXiv2022

Climate and Weather: Inspecting Depression Detection via Emotion Recognition

Automatic depression detection has attracted increasing amount of attention but remains a challenging task. Psychological research suggests that depressive mood is closely related with emotion expression and perception, which motivates the investigation of whether knowledge of emotion recognition can be transferred for depression detection. This paper uses pretrained features extracted from the emotion recognition model for depression detection, further fuses emotion modality with audio and text to form multimodal depression detection. The proposed emotion transfer improves depression detection performance on DAIC-WOZ as well as increases the training stability. The analysis of how the emotion expressed by depressed individuals is further perceived provides clues for further understanding of the relationship between depression and emotion.

cs.CL↗

arXiv2022

Audio-text Retrieval in Context

Audio-text retrieval based on natural language descriptions is a challenging task. It involves learning cross-modality alignments between long sequences under inadequate data conditions. In this work, we investigate several audio features as well as sequence aggregation methods for better audio-text alignment. Moreover, through a qualitative analysis we observe that semantic mapping is more important than temporal relations in contextual retrieval. Using pre-trained audio features and a descriptor-based aggregation method, we build our contextual audio-text retrieval system. Specifically, we utilize PANNs features pre-trained on a large sound event dataset and NetRVLAD pooling, which directly works with averaged descriptors. Experiments are conducted on the AudioCaps and CLOTHO datasets, and results are compared with the previous state-of-the-art system. With our proposed system, a significant improvement has been achieved on bidirectional audio-text retrieval, on all metrics including recall, median and mean rank.

cs.SD↗

arXiv2022

Can Audio Captions Be Evaluated with Image Caption Metrics?

Automated audio captioning aims at generating textual descriptions for an audio clip. To evaluate the quality of generated audio captions, previous works directly adopt image captioning metrics like SPICE and CIDEr, without justifying their suitability in this new domain, which may mislead the development of advanced models. This problem is still unstudied due to the lack of human judgment datasets on caption quality. Therefore, we firstly construct two evaluation benchmarks, AudioCaps-Eval and Clotho-Eval. They are established with pairwise comparison instead of absolute rating to achieve better inter-annotator agreement. Current metrics are found in poor correlation with human annotations on these datasets. To overcome their limitations, we propose a metric named FENSE, where we combine the strength of Sentence-BERT in capturing similarity, and a novel Error Detector to penalize erroneous sentences for robustness. On the newly established benchmarks, FENSE outperforms current metrics by 14-25% accuracy. Code, data and web demo available at: https://github.com/blmoistawinde/fense

cs.SD↗

arXiv2021

DEPA: Self-Supervised Audio Embedding for Depression Detection

Depression detection research has increased over the last few decades, one major bottleneck of which is the limited data availability and representation learning. Recently, self-supervised learning has seen success in pretraining text embeddings and has been applied broadly on related tasks with sparse data, while pretrained audio embeddings based on self-supervised learning are rarely investigated. This paper proposes DEPA, a self-supervised, pretrained depression audio embedding method for depression detection. An encoder-decoder network is used to extract DEPA on in-domain depressed datasets (DAIC and MDD) and out-domain (Switchboard, Alzheimer's) datasets. With DEPA as the audio embedding extracted at response-level, a significant performance gain is achieved on downstream tasks, evaluated on both sparse datasets like DAIC and large major depression disorder dataset (MDD). This paper not only exhibits itself as a novel embedding extracting method capturing response-level representation for depression detection but more significantly, is an exploration of self-supervised learning in a specific task within audio processing.

cs.HC↗

arXiv2021

Enriching Ontology with Temporal Commonsense for Low-Resource Audio Tagging

Audio tagging aims at predicting sound events occurred in a recording. Traditional models require enormous laborious annotations, otherwise performance degeneration will be the norm. Therefore, we investigate robust audio tagging models in low-resource scenarios with the enhancement of knowledge graphs. Besides existing ontological knowledge, we further propose a semi-automatic approach that can construct temporal knowledge graphs on diverse domain-specific label sets. Moreover, we leverage a variant of relation-aware graph neural network, D-GCN, to combine the strength of the two knowledge types. Experiments on AudioSet and SONYC urban sound tagging datasets suggest the effectiveness of the introduced temporal knowledge, and the advantage of the combined KGs with D-GCN over single knowledge source.

cs.SD↗

arXiv2021

Decoupled Dialogue Modeling and Semantic Parsing for Multi-Turn Text-to-SQL

Recently, Text-to-SQL for multi-turn dialogue has attracted great interest. Here, the user input of the current turn is parsed into the corresponding SQL query of the appropriate database, given all previous dialogue history. Current approaches mostly employ end-to-end models and consequently face two challenges. First, dialogue history modeling and Text-to-SQL parsing are implicitly combined, hence it is hard to carry out interpretable analysis and obtain targeted improvement. Second, SQL annotation of multi-turn dialogue is very expensive, leading to training data sparsity. In this paper, we propose a novel decoupled multi-turn Text-to-SQL framework, where an utterance rewrite model first explicitly solves completion of dialogue context, and then a single-turn Text-to-SQL parser follows. A dual learning approach is also proposed for the utterance rewrite model to address the data sparsity problem. Compared with end-to-end approaches, the proposed decoupled method can achieve excellent performance without any annotated in-domain data. With just a few annotated rewrite cases, the decoupled method outperforms the released state-of-the-art end-to-end models on both SParC and CoSQL datasets.

cs.CL↗

arXiv2021

Voice activity detection in the wild: A data-driven approach using teacher-student training

Voice activity detection is an essential pre-processing component for speech-related tasks such as automatic speech recognition (ASR). Traditional supervised VAD systems obtain frame-level labels from an ASR pipeline by using, e.g., a Hidden Markov model. These ASR models are commonly trained on clean and fully transcribed data, limiting VAD systems to be trained on clean or synthetically noised datasets. Therefore, a major challenge for supervised VAD systems is their generalization towards noisy, real-world data. This work proposes a data-driven teacher-student approach for VAD, which utilizes vast and unconstrained audio data for training. Unlike previous approaches, only weak labels during teacher training are required, enabling the utilization of any real-world, potentially noisy dataset. Our approach firstly trains a teacher model on a source dataset (Audioset) using clip-level supervision. After training, the teacher provides frame-level guidance to a student model on an unlabeled, target dataset. A multitude of student models trained on mid- to large-sized datasets are investigated (Audioset, Voxceleb, NIST SRE). Our approach is then respectively evaluated on clean, artificially noised, and real-world data. We observe significant performance gains in artificially noised and real-world scenarios. Lastly, we compare our approach against other unsupervised and supervised VAD methods, demonstrating our method's superiority.

cs.SD↗