The Video-Grounded Dialogue generation (VDG) is a challenging task requiring a comprehensive understanding of the multi-modal information to produce a pertinent response. However, VDG models may rely on dataset bias as a shortcut and fail to learn the multi-modal knowledge from both video and audio. Counterfactual reasoning is an effective method that can estimate and eliminate bias on some special aspects of classification tasks. However, conventional counterfactual reasoning cannot be applied to VDG tasks directly due to the BPE algorithm. In this paper, we reformulate the counterfactual reasoning from the information entropy perspective and extend it from the classification task to the generative task, which can effectively reduce the question-related bias in the auto-regressive generation task. We design CE-VDG to demonstrate the effectiveness in bias elimination of the reformulated counterfactual reasoning by using the proposed counterfactual entropy as an external loss. Extensive experiment results on two popular VDG datasets show the superiority of CE-VDG over the existing baseline method, demonstrating the effective debiasing capability in our model considering counterfactual entropy.

End-to-end (E2E) approach is gradually replacing hybrid models for automatic speech recognition (ASR) tasks. However, the optimization of E2E models lacks an intuitive method for handling decoding shifts, especially in scenarios with a large number of domain-specific rare words that hold specific important meanings. Furthermore, the absence of knowledge-intensive speech datasets in academia has been a significant limiting factor, and the commonly used speech corpora exhibit significant disparities with realistic conversation. To address these challenges, we present Medical Interview (MED-IT), a multi-turn consultation speech dataset that contains a substantial number of knowledge-intensive named entities. We also explore methods to enhance the recognition performance of rare words for E2E models. We propose a novel approach, post-decoder biasing, which constructs a transform probability matrix based on the distribution of training transcriptions. This guides the model to prioritize recognizing words in the biasing list. In our experiments, for subsets of rare words appearing in the training speech between 10 and 20 times, and between 1 and 5 times, the proposed method achieves a relative improvement of 9.3% and 5.1%, respectively.

Structured pruning is an effective technique for compressing pre-trained language models (PLMs), reducing model size and improving inference speed for efficient deployment. However, most of existing pruning algorithms require retraining, leading to additional computational overhead. While some retraining-free approaches have been proposed for classification tasks, they still require a fully fine-tuned model for the task, and may cause catastrophic performance degradation on generative tasks. To address these challenges, we propose P-pruning (pre-pruning), an innovative task-specific compression framework. P-pruning prunes redundant modules of PLMs before fine-tuning, reducing the costs associated with fine-tuning. We also introduce a pruning algorithm for this framework, which includes two techniques: (1) module clustering, which clusters the outputs of all heads and neurons based on the task input; and (2) centroid selection, which identifies the most salient element in each cluster and prunes the others. We apply our method to BERT and GPT-2 and evaluate its effectiveness on GLUE, SQuAD, WikiText-2, WikiText-103, and PTB datasets. Experimental results demonstrate that our approach achieves higher performance in both classification and generative tasks, while also reducing the time required for fine-tuning.

Although Non-autoregressive Transformer (NAT) models have achieved great success in terms of fast inference speed, this speedup comes with a performance drop due to the inherent multi-modality problem of the NAT model. Previous works commonly alleviate this problem by replacing the target side of the raw data with distilled data generated by Autoregressive Transformer (AT) models. However, the multi-modality problem in the distilled data is still significant and thus limits further improvement of the NAT models. In this paper, we propose a method called Sequence-Level Self-Distillation (SLSD), which aims to generate distilled data by the NAT model itself, eliminating the need for additional teacher networks. Furthermore, SLSD can adapt to different NAT models without precise adjustments since the self-distilled data is generated from the same types of NAT models. We conduct extensive experiments on WMT14 EN↔DE and WMT16 EN↔RO and choose four classic NAT models as the backbones to validate the generality and effectiveness of SLSD. The results show that our approach can consistently improve all models on both raw data and distilled data without sacrificing the inference speed.

Previous research for adapting a general neural machine translation (NMT) model into a specific domain usually neglects the diversity in translation within the same domain, which is a core problem for domain adaptation in real-world scenarios. One representative of such challenging scenarios is to deploy a translation system for a conference with a specific topic, e.g., global warming or coronavirus, where there are usually extremely less resources due to the limited schedule. To motivate wider investigation in such a scenario, we present a real-world fine-grained domain adaptation task in machine translation (FGraDA). The FGraDA dataset consists of Chinese-English translation task for four sub-domains of information technology: autonomous vehicles, AI education, real-time networks, and smart phone. Each sub-domain is equipped with a development set and test set for evaluation purposes. To be closer to reality, FGraDA does not employ any in-domain bilingual training data but provides bilingual dictionaries and wiki knowledge base, which can be easier obtained within a short time. We benchmark the fine-grained domain adaptation task and present in-depth analyses showing that there are still challenging problems to further improve the performance with heterogeneous resources.

This paper describes our speech translation system for the IWSLT 2018 Speech Translation of lectures and TED talks from English to German task. The pipeline approach is employed in our work, which mainly includes the Automatic Speech Recognition (ASR) system, a post-processing module, and the Neural Machine Translation (NMT) system. Our ASR system is an ensemble system of Deep-CNN, BLSTM, TDNN, N-gram Language model with lattice rescoring. We report average results on tst2013, tst2014, tst2015. Our best combination system has an average WER of 6.73. The machine translation system is based on Google’s Transformer architecture. We achieved an improvement of 3.6 BLEU over baseline system by applying several techniques, such as cleaning parallel corpus, fine tuning of single model, ensemble models and re-scoring with additional features. Our final average result on speech translation is 31.02 BLEU.