As a crucial task in the task-oriented dialogue systems, spoken language understanding (SLU) has garnered increasing attention. However, errors from automatic speech recognition (ASR) often hinder the performance of understanding. To tackle this problem, we propose MoE-SLU, an ASR-Robust SLU framework based on the mixture-of-experts technique. Specifically, we first introduce three strategies to generate additional transcripts from clean transcripts. Then, we employ the mixture-of-experts technique to weigh the representations of the generated transcripts, ASR transcripts, and the corresponding clean manual transcripts. Additionally, we also regularize the weighted average of predictions and the predictions of ASR transcripts by minimizing the Jensen-Shannon Divergence (JSD) between these two output distributions. Experiment results on three benchmark SLU datasets demonstrate that our MoE-SLU achieves state-of-the-art performance. Further model analysis also verifies the superiority of our method.

Spoken language understanding (SLU) inevitably suffers from error propagation from automatic speech recognition (ASR) in actual scenarios. Some recent works attempt to alleviate this issue through contrastive learning. However, they (1) sample negative pairs incorrectly in pre-training; (2) only focus on implicit metric learning while neglecting explicit erroneous predictions; (3) treat manual and ASR transcripts indiscriminately. In this paper, we propose a novel framework termed PCAD, which can calibrate bias and errors and achieve adaptive-balanced decoupling training. Specifically, PCAD utilizes a prototype-based loss to aggregate label and prediction priors and calibrate bias and error-prone semantics for better inter-class discrimination and intra-class consistency. We theoretically analyze the effect of this loss on robustness enhancement. Further, we leverage a teacher-student model for asymmetric decoupling training between different transcripts and formulate a novel gradient-sensitive exponential moving averaging (GS-EMA) algorithm for adaptive balance of accuracy and robustness. Experiments on three datasets show that PCAD significantly outperforms existing approaches and achieves new state-of-the-art performance.

Zero-shot cross-lingual spoken language understanding (SLU) can promote the globalization application of dialog systems, which has attracted increasing attention. While current code-switching based cross-lingual SLU frameworks have shown promising results, they (i) predominantly utilize contrastive objectives to model hard alignment, which may disrupt the inherent structure within sentences of each language; and (ii) focus optimization objectives solely on the original sentences, neglecting the relation between original sentences and code-switched sentences, which may hinder contextualized embeddings from further alignment. In this paper, we propose a novel framework dubbed REPE (short for Representation-Level and Prediction-Level Alignment), which leverages both code-switched and original sentences to achieve multi-level alignment. Specifically, REPE introduces optimal transport to facilitate soft alignment between the representations of code-switched and original sentences, thereby preserving structural integrity as much as possible. Moreover, REPE adopts multi-view learning to enforce consistency regularization between the prediction of the two sentences, aligning them into a more refined language-invariant space. Based on this, we further incorporate a self-distillation layer to boost the robustness of REPE. Extensive experiments on two benchmarks across ten languages demonstrate the superiority of the proposed REPE framework.

The visual question localized-answering (VQLA) system has garnered increasing attention due to its potential as a knowledgeable assistant in surgical education. Apart from providing text-based answers, VQLA can also pinpoint the specific region of interest for better surgical scene understanding. Although recent Transformer-based models for VQLA have obtained promising results, they (1) conduct vanilla text-to-image cross attention, leading to unidirectional and coarse-grained alignment; (2) ignore exploiting the semantics of answers to further boost performance. In this paper, we propose a novel model termed OTAS, which first introduces optimal transport to achieve bidirectional and fine-grained alignment between images and questions, enabling more precise localization. Besides, OTAS incorporates a set of learnable candidate answer embeddings to query the probability of each answer class for a given image-question pair. Through Transformer attention, the candidate answer embeddings interact with the fused features of the image-question pair to make the answer decision. Extensive experiments on two widely-used benchmark datasets demonstrate the superiority of our model over state-of-the-art methods.

In recent years, Multimodal Sentiment Analysis (MSA) leveraging deep learning has demonstrated exceptional performance in a wide range of domains. Its success lies in effectively utilizing information from multiple modalities to analyze sentiments. Despite these advancements, MSA is confronted with two significant challenges. Firstly, each modality often has a surplus of unimportance data, which can overshadow the essential information. Secondly, the crucial cues for sentiment analysis may conflict across different modalities, thereby complicating the analysis process. These issues have a certain impact on the model’s effectiveness in MSA tasks. To address these challenges, this paper introduces a novel method tailored for MSA, termed InfoEnh. This approach utilizes a masking technique as the bottleneck for information filtering, simultaneously maximizing mutual information to retain crucial data. Furthermore, the method integrates all modalities into a common feature space via domain adaptation, which is enhanced by the application of optimal transport. Extensive experiments conducted on two benchmark MSA datasets demonstrate the effectiveness of our proposed approach. Further analyzes indicate significant improvements over the baselines.

Dialogue-based relation extraction (DRE) aims to determine the semantic relation of a given pair of arguments from a piece of dialogue, which has received increasing attention. Due to the low information density of dialogue text, it is difficult for the model to focus on key information. To this end, in this paper, we propose a Knowledge-Enhanced Prompt-Tuning (KEPT) method to effectively enhance DRE model by exploiting trigger and label semantic. Specifically, we propose two beneficial tasks, masked trigger prediction, and verbalizer representation learning, to effectively inject trigger knowledge and label semantic knowledge respectively. Furthermore, we convert the DRE task to a masked language modeling task to unify the format of knowledge injection and utilization, aiming to better promote DRE performance. Experimental results on the DialogRE dataset show that our KEPT achieves state-of-the-art performance in F1 and F1c scores. Detailed analyses demonstrate the effectiveness and efficiency of our proposed approach. Code is available at https://github.com/blackbookay/KEPT.

Multi-modal sarcasm detection aims to identify whether a given sample with multi-modal information (i.e., text and image) is sarcastic, which has received increasing attention due to the rapid growth of multi-modal posts on modern social media. However, mainstream models process the input of each modality in a holistic manner, resulting in redundant and unrefined information. Moreover, the representations of different modalities are entangled in one common latent space to perform complex cross-modal interactions, neglecting the heterogeneity and distribution gap of different modalities. To address these issues, we propose a novel framework DMMD (short for Disentangled Multi-grained Multi-modal Distilling) for multi-modal sarcasm detection, which conducts multi-grained knowledge distilling (i.e., intra-subspace and inter-subspace) based on the disentangled multi-modal representations. Concretely, the representations of each modality are disentangled explicitly into modality-agnostic/specific subspaces. Then we transfer cross-modal knowledge by conducting intra-subspace knowledge distilling in a self-adaptive pattern. We also apply mutual learning to regularize the underlying inter-subspace consistency. Extensive experiments on a commonly used benchmark demonstrate the efficacy of our DMMD over cutting-edge methods. More encouragingly, visualization results indicate the multi-modal representations display meaningful distributional patterns, and we hope it will be helpful for the community of multi-modal knowledge transfer.

Zero-shot Spoken Language Understanding (SLU) aims to enable task-oriented dialogue systems to understand user needs without training data. Challenging but worthwhile, zero-shot SLU reduces the time and effort that data labeling takes. Recent advancements in large language models (LLMs), such as GPT3.5 and ChatGPT, have shown promising results in zero-shot settings, which motivates us to explore prompt-based methods. In this study, we investigate whether strong SLU models can be constructed by directly prompting LLMs. Specifically, we propose a simple yet effective two-stage framework dubbed GPT-SLU, which transforms the SLU task into a question-answering problem. Powered by multi-stage mutual guided prompts, GPT-SLU can leverage the correlations between two subtasks in SLU to achieve better predictions, which is greatly explored in the traditional fine-tuning paradigm. Experimental results on three SLU benchmark datasets demonstrate the significant potential of LLMs for zero-shot SLU. Comprehensive analyses validate the effectiveness of our proposed framework and also indicate that there is still room for further improvement of LLMs in SLU scenarios.

Joint intent detection and slot filling models have shown promising success in recent years due to the high correlations between the two tasks. However, previous works independently decode the two tasks, which could result in misaligned predictions for both tasks. To address this shortcoming, we propose a novel method named Label-aware Compact Linguistics Representation (LCLR), which leverages label embeddings to jointly guide the decoding process. Concretely, LCLR projects both task-specific hidden states into a joint label latent space, where both task-specific hidden states could be concisely represented as linear combinations of label embeddings. Such feature decomposition of task-specific hidden states increases the representing power for the linguistics of utterance. Extensive experiments on two single- and multi-intent SLU benchmarks prove that LCLR can learn more discriminative label information than previous separate decoders, and consistently outperform previous state-of-the-art methods across all metrics. More encouragingly, LCLR can be applied to boost the performance of existing approaches, making it easy to be incorporated into any existing SLU models.

Enhancing the robustness towards Automatic Speech Recognition (ASR) errors is of great importance for Spoken Language Understanding (SLU). Trending ASR-robust SLU systems have witnessed impressive improvements through global contrastive learning. However, although most ASR errors occur only at local positions of utterances, they can easily lead to severe semantic changes, and utterance-level classification or comparison is difficult to distinguish such differences. To address the problem, we propose a two-stage multi-grained contrastive learning framework dubbed MCLF. Technically, we first adapt the pre-trained language models to downstream SLU datasets via the proposed multi-grained contrastive learning objective and then fine-tune it on the corresponding dataset. Besides, to facilitate contrastive learning in the pre-training stage, we explore several data augmentation methods to expand the training data. Experimental results and detailed analyses on four datasets and four BERT-like backbone models demonstrate the effectiveness of our approach.

Spoken Language Understanding (SLU), a crucial component of task-oriented dialogue systems, has consistently garnered attention from both academic and industrial communities. Although incorporating syntactic information into models has the potential to enhance the comprehension of user utterances and yield impressive results, its application in SLU systems remains largely unexplored. In this paper, we propose a carefully designed model termed Syntax-aware attention (SAT) to enhance SLU, where attention scopes are constrained based on relationships within the syntactic structure. Experimental results on three datasets show that our model achieves substantial improvements and excellent performance. Moreover, SAT can be integrated into other BERT-based language models to further boost their performance.

Despite the success of spoken language understanding (SLU) in high-resource languages, achieving similar performance in low-resource settings, such as zero-shot scenarios, remains challenging due to limited labeled training data. To improve zero-shot cross-lingual SLU, recent studies have explored code-switched sentences containing tokens from multiple languages. However, vanilla code-switched sentences often lack semantic and grammatical coherence. We ascribe this lack to two issues: (1) randomly replacing code-switched tokens with equal probability and (2) disregarding token-level dependency within each language. To tackle these issues, in this paper, we propose a novel method termed SoGo, for zero-shot cross-lingual SLU. First, we use a saliency-based substitution approach to extract keywords as substitution options. Then, we introduce a novel token-level alignment strategy that considers the similarity between the context and the code-switched tokens, ensuring grammatical coherence in code-switched sentences. Extensive experiments and analyses demonstrate the superior performance of SoGo across nine languages on MultiATIS++.

Language understanding in speech-based systems has attracted extensive interest from both academic and industrial communities in recent years with the growing demand for voice-based applications. Prior works focus on independent research by the automatic speech recognition (ASR) and natural language processing (NLP) communities, or on jointly modeling the speech and NLP problems focusing on a single dataset or single NLP task. To facilitate the development of spoken language research, we introduce MTL-SLT, a multi-task learning framework for spoken language tasks. MTL-SLT takes speech as input, and outputs transcription, intent, named entities, summaries, and answers to text queries, supporting the tasks of spoken language understanding, spoken summarization and spoken question answering respectively. The proposed framework benefits from three key aspects: 1) pre-trained sub-networks of ASR model and language model; 2) multi-task learning objective to exploit shared knowledge from different tasks; 3) end-to-end training of ASR and downstream NLP task based on sequence loss. We obtain state-of-the-art results on spoken language understanding tasks such as SLURP and ATIS. Spoken summarization results are reported on a new dataset: Spoken-Gigaword.

Transformer-based pre-trained language models like BERT, though powerful in many tasks, are expensive in both memory and computation, due to their large number of parameters. Previous works show that some parameters in these models can be pruned away without severe accuracy drop. However, these redundant features contribute to a comprehensive understanding of the training data and removing them weakens the model’s representation ability. In this paper, we propose GhostBERT, which generates more features with very cheap operations from the remaining features. In this way, GhostBERT has similar memory and computational cost as the pruned model, but enjoys much larger representation power. The proposed ghost module can also be applied to unpruned BERT models to enhance their performance with negligible additional parameters and computation. Empirical results on the GLUE benchmark on three backbone models (i.e., BERT, RoBERTa and ELECTRA) verify the efficacy of our proposed method.

Recently, spoken language understanding (SLU) has attracted extensive research interests, and various SLU datasets have been proposed to promote the development. However, most of the existing methods focus on a single individual dataset, the efforts to improve the robustness of models and obtain better performance by combining the merits of various datasets are not well studied. In this paper, we argue that if these SLU datasets are considered together, different knowledge from different datasets could be learned jointly, and there are high chances to promote the performance of each dataset. At the same time, we further attempt to prevent data leakage when unifying multiple datasets which, arguably, is more useful in an industry setting. To this end, we propose a federated learning framework, which could unify various types of datasets as well as tasks to learn and fuse various types of knowledge, i.e., text representations, from different datasets and tasks, without the sharing of downstream task data. The fused text representations merge useful features from different SLU datasets and tasks and are thus much more powerful than the original text representations alone in individual tasks. At last, in order to provide multi-granularity text representations for our framework, we propose a novel Multi-view Encoder (MV-Encoder) as the backbone of our federated learning framework. Experiments on two SLU benchmark datasets, including two tasks (intention detection and slot filling) and federated learning settings (horizontal federated learning, vertical federated learning and federated transfer learning), demonstrate the effectiveness and universality of our approach. Specifically, we are able to get 1.53% improvement on the intent detection metric accuracy. And we could also boost the performance of a strong baseline by up to 5.29% on the slot filling metric F1. Furthermore, by leveraging BERT as an additional encoder, we establish new state-of-the-art results on SNIPS and ATIS datasets, where we get 99.33% and 98.28% in terms of accuracy on intent detection task as well as 97.20% and 96.41% in terms of F1 score on slot filling task, respectively.