ASCEND: A Spontaneous Chinese-English Dataset for Code-switching in Multi-turn Conversation

Code-switching is a speech phenomenon when a speaker switches language during a conversation. Despite the spontaneous nature of code-switching in conversational spoken language, most existing works collect code-switching data through read speech instead of spontaneous speech. ASCEND (A Spontaneous Chinese-English Dataset) introduces a high-quality resource of spontaneous multi-turn conversational dialogue Chinese-English code-switching corpus collected in Hong Kong. We report ASCEND's design and procedure of collecting the speech data, including the annotations in this work. ASCEND includes 23 bilinguals that are fluent in both Chinese and English and consists of 10.62 hours clean speech corpus.

NER-BERT: A Pre-trained Model for Low-Resource Entity Tagging

Named entity recognition (NER) models generally perform poorly when large training datasets are unavailable for low-resource domains. Recently, pre-training a large-scale language model has become a promising direction for coping with the data scarcity issue. However, the underlying discrepancies between the language modeling and NER task could limit the models' performance, and pre-training for the NER task has rarely been studied since the collected NER datasets are generally small or large but with low quality. In this paper, we construct a massive NER corpus with a relatively high quality, and we pre-train a NER-BERT model based on the created dataset. Experimental results show that our pre-trained model can significantly outperform BERT as well as other strong baselines in low-resource scenarios across nine diverse domains. Moreover, a visualization of entity representations further indicates the effectiveness of NER-BERT for categorizing a variety of entities.

Boosting Discriminative Visual Representation Learning with Scenario-Agnostic Mixup

Mixup is a popular data-dependent augmentation technique for deep neural networks, which contains two sub-tasks, mixup generation and classification. The community typically confines mixup to supervised learning (SL) and the objective of generation sub-task is fixed to the sampled pairs instead of considering the whole data manifold. To overcome such limitations, we systematically study the objectives of two sub-tasks and propose Scenario-Agostic Mixup for both SL and Self-supervised Learning (SSL) scenarios, named SAMix. Specifically, we hypothesize and verify the core objective of mixup generation as optimizing the local smoothness between two classes subject to global discrimination from other classes. Based on this discovery, $\eta$-balanced mixup loss is proposed for complementary training of the two sub-tasks. Meanwhile, the generation sub-task is parameterized as an optimizable module, Mixer, which utilizes an attention mechanism to generate mixed samples without label dependency. Extensive experiments on SL and SSL tasks demonstrate that SAMix consistently outperforms leading methods by a large margin.

Surrogate Representation Learning with Isometric Mapping for Gray-box Graph Adversarial Attacks

Gray-box graph attacks aim at disrupting the performance of the victim model by using inconspicuous attacks with limited knowledge of the victim model. The parameters of the victim model and the labels of the test nodes are invisible to the attacker. To obtain the gradient on the node attributes or graph structure, the attacker constructs an imaginary surrogate model trained under supervision. However, there is a lack of discussion on the training of surrogate models and the robustness of provided gradient information. The general node classification model loses the topology of the nodes on the graph, which is, in fact, an exploitable prior for the attacker. This paper investigates the effect of representation learning of surrogate models on the transferability of gray-box graph adversarial attacks. To reserve the topology in the surrogate embedding, we propose Surrogate Representation Learning with Isometric Mapping (SRLIM). By using Isometric mapping method, our proposed SRLIM can constrain the topological structure of nodes from the input layer to the embedding space, that is, to maintain the similarity of nodes in the propagation process. Experiments prove the effectiveness of our approach through the improvement in the performance of the adversarial attacks generated by the gradient-based attacker in untargeted poisoning gray-box setups.

Vision Guided Generative Pre-trained Language Models for Multimodal Abstractive Summarization

Multimodal abstractive summarization (MAS) models that summarize videos (vision modality) and their corresponding transcripts (text modality) are able to extract the essential information from massive multimodal data on the Internet. Recently, large-scale generative pre-trained language models (GPLMs) have been shown to be effective in text generation tasks. However, existing MAS models cannot leverage GPLMs' powerful generation ability. To fill this research gap, we aim to study two research questions: 1) how to inject visual information into GPLMs without hurting their generation ability; and 2) where is the optimal place in GPLMs to inject the visual information? In this paper, we present a simple yet effective method to construct vision guided (VG) GPLMs for the MAS task using attention-based add-on layers to incorporate visual information while maintaining their original text generation ability. Results show that our best model significantly surpasses the prior state-of-the-art model by 5.7 ROUGE-1, 5.3 ROUGE-2, and 5.1 ROUGE-L scores on the How2 dataset, and our visual guidance method contributes 83.6% of the overall improvement. Furthermore, we conduct thorough ablation studies to analyze the effectiveness of various modality fusion methods and fusion locations.

Powering Comparative Classification with Sentiment Analysis via Domain Adaptive Knowledge Transfer

We study Comparative Preference Classification (CPC) which aims at predicting whether a preference comparison exists between two entities in a given sentence and, if so, which entity is preferred over the other. High-quality CPC models can significantly benefit applications such as comparative question answering and review-based recommendations. Among the existing approaches, non-deep learning methods suffer from inferior performances. The state-of-the-art graph neural network-based ED-GAT (Ma et al., 2020) only considers syntactic information while ignoring the critical semantic relations and the sentiments to the compared entities. We proposed sentiment Analysis Enhanced COmparative Network (SAECON) which improves CPC ac-curacy with a sentiment analyzer that learns sentiments to individual entities via domain adaptive knowledge transfer. Experiments on the CompSent-19 (Panchenko et al., 2019) dataset present a significant improvement on the F1 scores over the best existing CPC approaches.

CAiRE in DialDoc21: Data Augmentation for Information-Seeking Dialogue System

Information-seeking dialogue systems, including knowledge identification and response generation, aim to respond to users with fluent, coherent, and informative responses based on users' needs, which. To tackle this challenge, we utilize data augmentation methods and several training techniques with the pre-trained language models to learn a general pattern of the task and thus achieve promising performance. In DialDoc21 competition, our system achieved 74.95 F1 score and 60.74 Exact Match score in subtask 1, and 37.72 SacreBLEU score in subtask 2. Empirical analysis is provided to explain the effectiveness of our approaches.

X2Parser: Cross-Lingual and Cross-Domain Framework for Task-Oriented Compositional Semantic Parsing

Task-oriented compositional semantic parsing (TCSP) handles complex nested user queries and serves as an essential component of virtual assistants. Current TCSP models rely on numerous training data to achieve decent performance but fail to generalize to low-resource target languages or domains. In this paper, we present X2Parser, a transferable Cross-lingual and Cross-domain Parser for TCSP. Unlike previous models that learn to generate the hierarchical representations for nested intents and slots, we propose to predict flattened intents and slots representations separately and cast both prediction tasks into sequence labeling problems. After that, we further propose a fertility-based slot predictor that first learns to dynamically detect the number of labels for each token, and then predicts the slot types. Experimental results illustrate that our model can significantly outperform existing strong baselines in cross-lingual and cross-domain settings, and our model can also achieve a good generalization ability on target languages of target domains. Furthermore, our model tackles the problem in an efficient non-autoregressive way that reduces the latency by up to 66% compared to the generative model.