Coreference resolution is the task of identifying and clustering mentions that refer to the same entity in a document. Based on state-of-the-art deep learning approaches, end-to-end coreference resolution considers all spans as candidate mentions and tackles mention detection and coreference resolution simultaneously. Recently, researchers have attempted to incorporate document-level context using higher-order inference (HOI) to improve end-to-end coreference resolution. However, HOI methods have been shown to have marginal or even negative impact on coreference resolution. In this paper, we reveal the reasons for the negative impact of HOI coreference resolution. Contextualized representations (e.g., those produced by BERT) for building span embeddings have been shown to be highly anisotropic. We show that HOI actually increases and thus worsens the anisotropy of span embeddings and makes it difficult to distinguish between related but distinct entities (e.g., pilots and flight attendants). Instead of using HOI, we propose two methods, Less-Anisotropic Internal Representations (LAIR) and Data Augmentation with Document Synthesis and Mention Swap (DSMS), to learn less-anisotropic span embeddings for coreference resolution. LAIR uses a linear aggregation of the first layer and the topmost layer of contextualized embeddings. DSMS generates more diversified examples of related but distinct entities by synthesizing documents and by mention swapping. Our experiments show that less-anisotropic span embeddings improve the performance significantly (+2.8 F1 gain on the OntoNotes benchmark) reaching new state-of-the-art performance on the GAP dataset.

Here we present an improved approach for automated annotation of New Testament corpora with cross-lingual semantic concordance based on Strong’s numbers. Based on already annotated texts, they provide references to the original Greek words. Since scientific editions and translations of biblical texts are often not available for scientific purposes and are rarely freely available, there is a lack of up-to-date training data. In addition, since annotation, curation, and quality control of alignments between these texts are expensive, there is a lack of available biblical resources for scholars. We present two improved approaches to the problem, based on dictionaries and already annotated biblical texts. We provide a detailed evaluation of annotated and unannotated translations. We also discuss a proof of concept based on English and German New Testament translations. The results presented in this paper are novel and, to our knowledge, unique. They show promising performance, although further research is needed.

Automatic dialog systems have become a mainstream part of online customer service. Many such systems are built, maintained, and improved by customer service specialists, rather than dialog systems engineers and computer programmers. As conversations between people and machines become commonplace, it is critical to understand what is working, what is not, and what actions can be taken to reduce the frequency of inappropriate system responses. These analyses and recommendations need to be presented in terms that directly reflect the user experience rather than the internal dialog processing.

This paper introduces and explains the use of Actionable Conversational Quality Indicators (ACQIs), which are used both to recognize parts of dialogs that can be improved and to recommend how to improve them. This combines benefits of previous approaches, some of which have focused on producing dialog quality scoring while others have sought to categorize the types of errors the dialog system is making. We demonstrate the effectiveness of using ACQIs on LivePerson internal dialog systems used in commercial customer service applications and on the publicly available LEGOv2 conversational dataset. We report on the annotation and analysis of conversational datasets showing which ACQIs are important to fix in various situations.

The annotated datasets are then used to build a predictive model which uses a turn-based vector embedding of the message texts and achieves a 79% weighted average f1-measure at the task of finding the correct ACQI for a given conversation. We predict that if such a model worked perfectly, the range of potential improvement actions a bot-builder must consider at each turn could be reduced by an average of 81%.

Understanding the nature of meaning and its extensions (with metaphor as one typical kind) has been one core issue in figurative language study since Aristotle’s time. This research takes a computational cognitive perspective to model metaphor based on the assumption that meaning is perceptual, embodied, and encyclopedic. We model word meaning representation for metaphor detection with embodiment information obtained from behavioral experiments. Our work is the first attempt to incorporate sensorimotor knowledge into neural networks for metaphor detection, and demonstrates superiority, consistency, and interpretability compared to peer systems based on two general datasets. In addition, with cross-sectional analysis of different feature schemas, our results suggest that metaphor, as a device of cognitive conceptualization, can be ‘learned’ from the perceptual and actional information independent of several more explicit levels of linguistic representation. The access to such knowledge allows us to probe further into word meaning mapping tendencies relevant to our conceptualization and reaction to the physical world.

Conversational recommender system (CRS) needs to be seamlessly integrated between the two modules of recommendation and dialog, aiming to recommend high-quality items to users through multiple rounds of interactive dialogs. Items can typically refer to goods, movies, news, etc. Through this form of interactive dialog, users can express their preferences in real time, and the system can fully understand the user’s thoughts and recommend corresponding items. Although mainstream dialog recommendation systems have improved the performance to some extent, there are still some key issues, such as insufficient consideration of the entity’s order in the dialog, the different contributions of items in the dialog history, and the low diversity of generated responses. To address these shortcomings, we propose an improved dialog context model based on time-series features. Firstly, we augment the semantic representation of words and items using two external knowledge graphs and align the semantic space using mutual information maximization techniques. Secondly, we add a retrieval model to the dialog recommendation system to provide auxiliary information for generating replies. We then utilize a deep timing network to serialize the dialog content and more accurately learn the feature relationship between users and items for recommendation. In this paper, the dialog recommendation system is divided into two components, and different evaluation indicators are used to evaluate the performance of the dialog component and the recommendation component. Experimental results on widely used benchmarks show that the proposed method is effective.

A competitive and cost-effective public procurement (PP) process is essential for the effective use of public resources. In this work, we explore whether descriptions of procurement calls can be used to predict their outcomes. In particular, we focus on predicting four well-known economic metrics: (i) the number of offers, (ii) whether only a single offer is received, (iii) whether a foreign firm is awarded the contract, and (iv) whether the contract price exceeds the expected price. We extract the European Union’s multilingual PP notices, covering 22 different languages. We investigate fine-tuning multilingual transformer models and propose two approaches: (1) multilayer perceptron (MLP) models with transformer embeddings for each business sector in which the training data are filtered based on the procurement category and (2) a k-nearest neighbor (KNN)-based approach fine-tuned using triplet networks. The fine-tuned MBERT model outperforms all other models in predicting calls with a single offer and foreign contract awards, whereas our MLP-based filtering approach yields state-of-the-art results in predicting contracts in which the contract price exceeds the expected price. Furthermore, our KNN-based approach outperforms all the baselines in all tasks and our other proposed models in predicting the number of offers. Moreover, we investigate cross-lingual and multilingual training for our tasks and observe that multilingual training improves prediction accuracy in all our tasks. Overall, our experiments suggest that notice descriptions play an important role in the outcomes of PP calls.

Anti-Asian speech during the COVID-19 pandemic has been a serious problem with severe consequences. A hate speech wave swept social media platforms. The timely detection of Anti-Asian COVID-19-related hate speech is of utmost importance, not only to allow the application of preventive mechanisms but also to anticipate and possibly prevent other similar discriminatory situations. In this paper, we address the problem of detecting Anti-Asian COVID-19-related hate speech from social media data. Previous approaches that tackled this problem used a transformer-based model, BERT/RoBERTa, trained on the homologous annotated dataset and achieved good performance on this task. However, this requires extensive and annotated datasets with a strong connection to the topic. Both goals are difficult to meet without employing reliable, vast, and costly resources. In this paper, we propose a robust semi-supervised model, SSL-GAN-RoBERTa, that learns from a limited heterogeneous dataset and whose performance is further enhanced by using vast amounts of unlabeled data from another related domain. Compared with the RoBERTa baseline model, the experimental results show that the model has substantial performance gains in terms of Accuracy and Macro-F1 score in different scenarios that use data from different domains. Our proposed model achieves state-of-the-art performance results while efficiently using unlabeled data, showing promising applicability to other complex classification tasks where large amounts of labeled examples are difficult to obtain.