Commonsense question answering (QA) requires machines to utilize the QA content and external commonsense knowledge graph (KG) for reasoning when answering questions. Existing work uses two independent modules to model the QA contextual text representation and relationships between QA entities in KG, which prevents information sharing between modules for co-reasoning. In this paper, we propose a novel model, Co-Reasoning Network (CORN), which adopts a bidirectional multi-level connection structure based on Co-Attention Transformer. The structure builds bridges to connect each layer of the text encoder and graph encoder, which can introduce the QA entity relationship from KG to the text encoder and bring contextual text information to the graph encoder, so that these features can be deeply interactively fused to form comprehensive text and graph node representations. Meanwhile, we propose a QA-aware node based KG subgraph construction method. The QA-aware nodes aggregate the question entity nodes and the answer entity nodes, and further guide the expansion and construction process of the subgraph to enhance the connectivity and reduce the introduction of noise. We evaluate our model on QA benchmarks in the CommonsenseQA and OpenBookQA datasets, and CORN achieves state-of-the-art performance.

Noise Learning is important in the task of text classification which depends on massive labeled data that could be error-prone. However, we find that noise learning in text classification is relatively underdeveloped: 1. many methods that have been proven effective in the image domain are not explored in text classification, 2. it is difficult to conduct a fair comparison between previous studies as they do experiments in different noise settings. In this work, we adapt four state-of-the-art methods of noise learning from the image domain to text classification. Moreover, we conduct comprehensive experiments on our benchmark of noise learning with seven commonly-used methods, four datasets, and five noise modes. Additionally, most previous works are based on an implicit hypothesis that the commonly-used datasets such as TREC, Ag-News and Chnsenticorp contain no errors. However, these datasets indeed contain 0.61% to 15.77% noise labels which we define as intrinsic noise that can cause inaccurate evaluation. Therefore, we build a new dataset Golden-Chnsenticorp( G-Chnsenticorp) without intrinsic noise to more accurately compare the effects of different noise learning methods. To the best of our knowledge, this is the first benchmark of noise learning for text classification.

Meta-learning has emerged as an effective approach for few-shot text classification. However, current studies fail to realize the importance of the semantic interaction between sentence features and neglect to enhance the generalization ability of the model to new tasks. In this paper, we integrate an adversarial network architecture into the meta-learning system and leverage cost-effective modules to build a novel few-shot classification framework named SaAML. Significantly, our approach can exploit the temporal convolutional network to encourage more discriminative representation learning and explore the attention mechanism to promote more comprehensive feature expression, thus resulting in better adaptation for new classes. Through a series of experiments on four benchmark datasets, we demonstrate that our new framework acquires considerable superiority over state-of-the-art methods in all datasets, increasing the performance of 1-shot classification and 5-shot classification by 7.15% and 2.89%, respectively.

Out-of-scope intent detection is of practical importance in task-oriented dialogue systems. Since the distribution of outlier utterances is arbitrary and unknown in the training stage, existing methods commonly rely on strong assumptions on data distribution such as mixture of Gaussians to make inference, resulting in either complex multi-step training procedures or hand-crafted rules such as confidence threshold selection for outlier detection. In this paper, we propose a simple yet effective method to train an out-of-scope intent classifier in a fully end-to-end manner by simulating the test scenario in training, which requires no assumption on data distribution and no additional post-processing or threshold setting. Specifically, we construct a set of pseudo outliers in the training stage, by generating synthetic outliers using inliner features via self-supervision and sampling out-of-scope sentences from easily available open-domain datasets. The pseudo outliers are used to train a discriminative classifier that can be directly applied to and generalize well on the test task. We evaluate our method extensively on four benchmark dialogue datasets and observe significant improvements over state-of-the-art approaches. Our code has been released at https://github.com/liam0949/DCLOOS.

Multi-hop inference for explanation generation is to combine two or more facts to make an inference. The task focuses on generating explanations for elementary science questions. In the task, the relevance between the explanations and the QA pairs is of vital importance. To address the task, a three-step framework is proposed. Firstly, vector distance between two texts is utilized to recall the top-K relevant explanations for each question, reducing the calculation consumption. Then, a selection module is employed to choose those most relative facts in an autoregressive manner, giving a preliminary order for the retrieved facts. Thirdly, we adopt a re-ranking module to re-rank the retrieved candidate explanations with relevance between each fact and the QA pairs. Experimental results illustrate the effectiveness of the proposed framework with an improvement of 39.78% in NDCG over the official baseline.

Embedding-based entity alignment has been widely investigated in recent years, but most proposed methods still rely on an ideal supervised learning setting with a large number of unbiased seed mappings for training and validation, which significantly limits their usage. In this study, we evaluate those state-of-the-art methods in an industrial context, where the impact of seed mappings with different sizes and different biases is explored. Besides the popular benchmarks from DBpedia and Wikidata, we contribute and evaluate a new industrial benchmark that is extracted from two heterogeneous knowledge graphs (KGs) under deployment for medical applications. The experimental results enable the analysis of the advantages and disadvantages of these alignment methods and the further discussion of suitable strategies for their industrial deployment.

We present our 7th place solution to the Gendered Pronoun Resolution challenge, which uses BERT without fine-tuning and a novel augmentation strategy designed for contextual embedding token-level tasks. Our method anonymizes the referent by replacing candidate names with a set of common placeholder names. Besides the usual benefits of effectively increasing training data size, this approach diversifies idiosyncratic information embedded in names. Using same set of common first names can also help the model recognize names better, shorten token length, and remove gender and regional biases associated with names. The system scored 0.1947 log loss in stage 2, where the augmentation contributed to an improvements of 0.04. Post-competition analysis shows that, when using different embedding layers, the system scores 0.1799 which would be third place.

Paraphrase generation is important in various applications such as search, summarization, and question answering due to its ability to generate textual alternatives while keeping the overall meaning intact. Clinical paraphrase generation is especially vital in building patient-centric clinical decision support (CDS) applications where users are able to understand complex clinical jargons via easily comprehensible alternative paraphrases. This paper presents Neural Clinical Paraphrase Generation (NCPG), a novel approach that casts the task as a monolingual neural machine translation (NMT) problem. We propose an end-to-end neural network built on an attention-based bidirectional Recurrent Neural Network (RNN) architecture with an encoder-decoder framework to perform the task. Conventional bilingual NMT models mostly rely on word-level modeling and are often limited by out-of-vocabulary (OOV) issues. In contrast, we represent the source and target paraphrase pairs as character sequences to address this limitation. To the best of our knowledge, this is the first work that uses attention-based RNNs for clinical paraphrase generation and also proposes an end-to-end character-level modeling for this task. Extensive experiments on a large curated clinical paraphrase corpus show that the attention-based NCPG models achieve improvements of up to 5.2 BLEU points and 0.5 METEOR points over a non-attention based strong baseline for word-level modeling, whereas further gains of up to 6.1 BLEU points and 1.3 METEOR points are obtained by the character-level NCPG models over their word-level counterparts. Overall, our models demonstrate comparable performance relative to the state-of-the-art phrase-based non-neural models.

Essential grammatical information is conveyed in signed languages by clusters of events involving facial expressions and movements of the head and upper body. This poses a significant challenge for computer-based sign language recognition. Here, we present new methods for the recognition of nonmanual grammatical markers in American Sign Language (ASL) based on: (1) new 3D tracking methods for the estimation of 3D head pose and facial expressions to determine the relevant low-level features; (2) methods for higher-level analysis of component events (raised/lowered eyebrows, periodic head nods and head shakes) used in grammatical markings―with differentiation of temporal phases (onset, core, offset, where appropriate), analysis of their characteristic properties, and extraction of corresponding features; (3) a 2-level learning framework to combine low- and high-level features of differing spatio-temporal scales. This new approach achieves significantly better tracking and recognition results than our previous methods.

This paper addresses the problem of automatically recognizing linguistically significant nonmanual expressions in American Sign Language from video. We develop a fully automatic system that is able to track facial expressions and head movements, and detect and recognize facial events continuously from video. The main contributions of the proposed framework are the following: (1) We have built a stochastic and adaptive ensemble of face trackers to address factors resulting in lost face track; (2) We combine 2D and 3D deformable face models to warp input frames, thus correcting for any variation in facial appearance resulting from changes in 3D head pose; (3) We use a combination of geometric features and texture features extracted from a canonical frontal representation. The proposed new framework makes it possible to detect grammatically significant nonmanual expressions from continuous signing and to differentiate successfully among linguistically significant expressions that involve subtle differences in appearance. We present results that are based on the use of a dataset containing 330 sentences from videos that were collected and linguistically annotated at Boston University.