Aspect-based sentiment analysis (ABSA) is a natural language processing problem that requires analyzing user-generated reviews in order to determine: a) The target entity being reviewed, b) The high-level aspect to which it belongs, and c) The sentiment expressed toward the targets and the aspects. Numerous yet scattered corpora for ABSA make it difficult for researchers to quickly identify corpora best suited for a specific ABSA subtask. This study aims to present a database of corpora that can be used to train and assess autonomous ABSA systems. Additionally, we provide an overview of the major corpora concerning the various ABSA and its subtasks and highlight several corpus features that researchers should consider when selecting a corpus. We conclude that further large-scale ABSA corpora are required. Additionally, because each corpus is constructed differently, it is time-consuming for researchers to experiment with a novel ABSA algorithm on many corpora and often employ just one or a few corpora. The field would benefit from an agreement on a data standard for ABSA corpora. Finally, we discuss the advantages and disadvantages of current collection approaches and make recommendations for future ABSA dataset gathering.
Dense retrieval models using a transformer-based bi-encoder design have emerged as an active area of research. In this work, we focus on the task of monolingual retrieval in a variety of typologically diverse languages using one such design. Although recent work with multilingual transformers demonstrates that they exhibit strong cross-lingual generalization capabilities, there remain many open research questions, which we tackle here. Our study is organized as a "best practices" guide for training multilingual dense retrieval models, broken down into three main scenarios: where a multilingual transformer is available, but relevance judgments are not available in the language of interest; where both models and training data are available; and, where training data are available not but models. In considering these scenarios, we gain a better understanding of the role of multi-stage fine-tuning, the strength of cross-lingual transfer under various conditions, the usefulness of out-of-language data, and the advantages of multilingual vs. monolingual transformers. Our recommendations offer a guide for practitioners building search applications, particularly for low-resource languages, and while our work leaves open a number of research questions, we provide a solid foundation for future work.
Knowledge graph completion (a.k.a.~link prediction), i.e.,~the task of inferring missing information from knowledge graphs, is a widely used task in many applications, such as product recommendation and question answering. The state-of-the-art approaches of knowledge graph embeddings and/or rule mining and reasoning are data-driven and, thus, solely based on the information the input knowledge graph contains. This leads to unsatisfactory prediction results which make such solutions inapplicable to crucial domains such as healthcare. To further enhance the accuracy of knowledge graph completion we propose to loosely-couple the data-driven power of knowledge graph embeddings with domain-specific reasoning stemming from experts or entailment regimes (e.g., OWL2). In this way, we not only enhance the prediction accuracy with domain knowledge that may not be included in the input knowledge graph but also allow users to plugin their own knowledge graph embedding and reasoning method. Our initial results show that we enhance the MRR accuracy of vanilla knowledge graph embeddings by up to 3x and outperform hybrid solutions that combine knowledge graph embeddings with rule mining and reasoning up to 3.5x MRR.
Musical genre's classification has been a relevant research topic. The association between music and genres is fundamental for the media industry, which manages musical recommendation systems, and for music streaming services, which may appear classified by genres. In this context, this work presents a feature extraction method for the automatic classification of musical genres, based on complex networks and their topological measurements. The proposed method initially converts the musics into sequences of musical notes and then maps the sequences as complex networks. Topological measurements are extracted to characterize the network topology, which composes a feature vector that applies to the classification of musical genres. The method was evaluated in the classification of 10 musical genres by adopting the GTZAN dataset and 8 musical genres by adopting the FMA dataset. The results were compared with methods in the literature. The proposed method outperformed all compared methods by presenting high accuracy and low standard deviation, showing its suitability for the musical genre's classification, which contributes to the media industry in the automatic classification with assertiveness and robustness. The proposed method is implemented in an open source in the Python language and freely available at https://github.com/omatheuspimenta/examinner.
Multi-modal Ads Video Understanding Challenge is the first grand challenge aiming to comprehensively understand ads videos. Our challenge includes two tasks: video structuring in the temporal dimension and multi-modal video classification. It asks the participants to accurately predict both the scene boundaries and the multi-label categories of each scene based on a fine-grained and ads-related category hierarchy. Therefore, our task has four distinguishing features from previous ones: ads domain, multi-modal information, temporal segmentation, and multi-label classification. It will advance the foundation of ads video understanding and have a significant impact on many ads applications like video recommendation. This paper presents an overview of our challenge, including the background of ads videos, an elaborate description of task and dataset, evaluation protocol, and our proposed baseline. By ablating the key components of our baseline, we would like to reveal the main challenges of this task and provide useful guidance for future research of this area. In this paper, we give an extended version of our challenge overview. The dataset will be publicly available at https://algo.qq.com/.
Active learning promises to alleviate the massive data needs of supervised machine learning: it has successfully improved sample efficiency by an order of magnitude on traditional tasks like topic classification and object recognition. However, we uncover a striking contrast to this promise: across 5 models and 4 datasets on the task of visual question answering, a wide variety of active learning approaches fail to outperform random selection. To understand this discrepancy, we profile 8 active learning methods on a per-example basis, and identify the problem as collective outliers -- groups of examples that active learning methods prefer to acquire but models fail to learn (e.g., questions that ask about text in images or require external knowledge). Through systematic ablation experiments and qualitative visualizations, we verify that collective outliers are a general phenomenon responsible for degrading pool-based active learning. Notably, we show that active learning sample efficiency increases significantly as the number of collective outliers in the active learning pool decreases. We conclude with a discussion and prescriptive recommendations for mitigating the effects of these outliers in future work.
Classifiers commonly make use of pre-annotated datasets, wherein a model is evaluated by pre-defined metrics on a held-out test set typically made of human-annotated labels. Metrics used in these evaluations are tied to the availability of well-defined ground truth labels, and these metrics typically do not allow for inexact matches. These noisy ground truth labels and strict evaluation metrics may compromise the validity and realism of evaluation results. In the present work, we discuss these concerns and conduct a systematic posthoc verification experiment on the entity linking (EL) task. Unlike traditional methodologies, which asks annotators to provide free-form annotations, we ask annotators to verify the correctness of annotations after the fact (i.e., posthoc). Compared to pre-annotation evaluation, state-of-the-art EL models performed extremely well according to the posthoc evaluation methodology. Posthoc validation also permits the validation of the ground truth dataset. Surprisingly, we find predictions from EL models had a similar or higher verification rate than the ground truth. We conclude with a discussion on these findings and recommendations for future evaluations.
While AI has benefited humans, it may also harm humans if not appropriately developed. We conducted a literature review of current related work in developing AI systems from an HCI perspective. Different from other approaches, our focus is on the unique characteristics of AI technology and the differences between non-AI computing systems and AI systems. We further elaborate on the human-centered AI (HCAI) approach that we proposed in 2019. Our review and analysis highlight unique issues in developing AI systems which HCI professionals have not encountered in non-AI computing systems. To further enable the implementation of HCAI, we promote the research and application of human-AI interaction (HAII) as an interdisciplinary collaboration. There are many opportunities for HCI professionals to play a key role to make unique contributions to the main HAII areas as we identified. To support future HCI practice in the HAII area, we also offer enhanced HCI methods and strategic recommendations. In conclusion, we believe that promoting the HAII research and application will further enable the implementation of HCAI, enabling HCI professionals to address the unique issues of AI systems and develop human-centered AI systems.
Reinforcement learning typically assumes that the agent observes feedback from the environment immediately, but in many real-world applications (like recommendation systems) the feedback is observed in delay. Thus, we consider online learning in episodic Markov decision processes (MDPs) with unknown transitions, adversarially changing costs and unrestricted delayed feedback. That is, the costs and trajectory of episode $k$ are only available at the end of episode $k + d^k$, where the delays $d^k$ are neither identical nor bounded, and are chosen by an adversary. We present novel algorithms based on policy optimization that achieve near-optimal high-probability regret of $\widetilde O ( \sqrt{K} + \sqrt{D} )$ under full-information feedback, where $K$ is the number of episodes and $D = \sum_{k} d^k$ is the total delay. Under bandit feedback, we prove similar $\widetilde O ( \sqrt{K} + \sqrt{D} )$ regret assuming that the costs are stochastic, and $\widetilde O ( K^{2/3} + D^{2/3} )$ regret in the general case. To our knowledge, we are the first to consider the important setting of delayed feedback in adversarial MDPs.
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