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Temporal Relation Extraction with a Graph-Based Deep Biaffine Attention Model

Jan 16, 2022
Bo-Ying Su, Shang-Ling Hsu, Kuan-Yin Lai, Amarnath Gupta

Temporal information extraction plays a critical role in natural language understanding. Previous systems have incorporated advanced neural language models and have successfully enhanced the accuracy of temporal information extraction tasks. However, these systems have two major shortcomings. First, they fail to make use of the two-sided nature of temporal relations in prediction. Second, they involve non-parallelizable pipelines in inference process that bring little performance gain. To this end, we propose a novel temporal information extraction model based on deep biaffine attention to extract temporal relationships between events in unstructured text efficiently and accurately. Our model is performant because we perform relation extraction tasks directly instead of considering event annotation as a prerequisite of relation extraction. Moreover, our architecture uses Multilayer Perceptrons (MLP) with biaffine attention to predict arcs and relation labels separately, improving relation detecting accuracy by exploiting the two-sided nature of temporal relationships. We experimentally demonstrate that our model achieves state-of-the-art performance in temporal relation extraction.


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Towards Zero-shot Sign Language Recognition

Jan 15, 2022
Yunus Can Bilge, Ramazan Gokberk Cinbis, Nazli Ikizler-Cinbis

This paper tackles the problem of zero-shot sign language recognition (ZSSLR), where the goal is to leverage models learned over the seen sign classes to recognize the instances of unseen sign classes. In this context, readily available textual sign descriptions and attributes collected from sign language dictionaries are utilized as semantic class representations for knowledge transfer. For this novel problem setup, we introduce three benchmark datasets with their accompanying textual and attribute descriptions to analyze the problem in detail. Our proposed approach builds spatiotemporal models of body and hand regions. By leveraging the descriptive text and attribute embeddings along with these visual representations within a zero-shot learning framework, we show that textual and attribute based class definitions can provide effective knowledge for the recognition of previously unseen sign classes. We additionally introduce techniques to analyze the influence of binary attributes in correct and incorrect zero-shot predictions. We anticipate that the introduced approaches and the accompanying datasets will provide a basis for further exploration of zero-shot learning in sign language recognition.


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Structure with Semantics: Exploiting Document Relations for Retrieval

Jan 12, 2022
Natraj Raman, Sameena Shah, Manuela Veloso

Retrieving relevant documents from a corpus is typically based on the semantic similarity between the document content and query text. The inclusion of structural relationship between documents can benefit the retrieval mechanism by addressing semantic gaps. However, incorporating these relationships requires tractable mechanisms that balance structure with semantics and take advantage of the prevalent pre-train/fine-tune paradigm. We propose here a holistic approach to learning document representations by integrating intra-document content with inter-document relations. Our deep metric learning solution analyzes the complex neighborhood structure in the relationship network to efficiently sample similar/dissimilar document pairs and defines a novel quintuplet loss function that simultaneously encourages document pairs that are semantically relevant to be closer and structurally unrelated to be far apart in the representation space. Furthermore, the separation margins between the documents are varied flexibly to encode the heterogeneity in relationship strengths. The model is fully fine-tunable and natively supports query projection during inference. We demonstrate that it outperforms competing methods on multiple datasets for document retrieval tasks.


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Challenge Dataset of Cognates and False Friend Pairs from Indian Languages

Dec 17, 2021
Diptesh Kanojia, Pushpak Bhattacharyya, Malhar Kulkarni, Gholamreza Haffari

Cognates are present in multiple variants of the same text across different languages (e.g., "hund" in German and "hound" in English language mean "dog"). They pose a challenge to various Natural Language Processing (NLP) applications such as Machine Translation, Cross-lingual Sense Disambiguation, Computational Phylogenetics, and Information Retrieval. A possible solution to address this challenge is to identify cognates across language pairs. In this paper, we describe the creation of two cognate datasets for twelve Indian languages, namely Sanskrit, Hindi, Assamese, Oriya, Kannada, Gujarati, Tamil, Telugu, Punjabi, Bengali, Marathi, and Malayalam. We digitize the cognate data from an Indian language cognate dictionary and utilize linked Indian language Wordnets to generate cognate sets. Additionally, we use the Wordnet data to create a False Friends' dataset for eleven language pairs. We also evaluate the efficacy of our dataset using previously available baseline cognate detection approaches. We also perform a manual evaluation with the help of lexicographers and release the curated gold-standard dataset with this paper.

* Published at LREC 2020 

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HydraText: Multi-objective Optimization for Adversarial Textual Attack

Nov 02, 2021
Shengcai Liu, Ning Lu, Cheng Chen, Chao Qian, Ke Tang

The field of adversarial textual attack has significantly grown over the last years, where the commonly considered objective is to craft adversarial examples that can successfully fool the target models. However, the imperceptibility of attacks, which is also an essential objective, is often left out by previous studies. In this work, we advocate considering both objectives at the same time, and propose a novel multi-optimization approach (dubbed HydraText) with provable performance guarantee to achieve successful attacks with high imperceptibility. We demonstrate the efficacy of HydraText through extensive experiments under both score-based and decision-based settings, involving five modern NLP models across five benchmark datasets. In comparison to existing state-of-the-art attacks, HydraText consistently achieves simultaneously higher success rates, lower modification rates, and higher semantic similarity to the original texts. A human evaluation study shows that the adversarial examples crafted by HydraText maintain validity and naturality well. Finally, these examples also exhibit good transferability and can bring notable robustness improvement to the target models by adversarial training.


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Identifiable Variational Autoencoders via Sparse Decoding

Oct 20, 2021
Gemma E. Moran, Dhanya Sridhar, Yixin Wang, David M. Blei

We develop the Sparse VAE, a deep generative model for unsupervised representation learning on high-dimensional data. Given a dataset of observations, the Sparse VAE learns a set of latent factors that captures its distribution. The model is sparse in the sense that each feature of the dataset (i.e., each dimension) depends on a small subset of the latent factors. As examples, in ratings data each movie is only described by a few genres; in text data each word is only applicable to a few topics; in genomics, each gene is active in only a few biological processes. We first show that the Sparse VAE is identifiable: given data drawn from the model, there exists a uniquely optimal set of factors. (In contrast, most VAE-based models are not identifiable.) The key assumption behind Sparse-VAE identifiability is the existence of "anchor features", where for each factor there exists a feature that depends only on that factor. Importantly, the anchor features do not need to be known in advance. We then show how to fit the Sparse VAE with variational EM. Finally, we empirically study the Sparse VAE with both simulated and real data. We find that it recovers meaningful latent factors and has smaller heldout reconstruction error than related methods.


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Pre-trained Language Models in Biomedical Domain: A Systematic Survey

Oct 12, 2021
Benyou Wang, Qianqian Xie, Jiahuan Pei, Prayag Tiwari, Zhao Li, Jie fu

Pre-trained language models (PLMs) have been the de facto paradigm for most natural language processing (NLP) tasks. This also benefits biomedical domain: researchers from informatics, medicine, and computer science (CS) communities propose various PLMs trained on biomedical datasets, e.g., biomedical text, electronic health records, protein, and DNA sequences for various biomedical tasks. However, the cross-discipline characteristics of biomedical PLMs hinder their spreading among communities; some existing works are isolated from each other without comprehensive comparison and discussions. It expects a survey that not only systematically reviews recent advances of biomedical PLMs and their applications but also standardizes terminology and benchmarks. In this paper, we summarize the recent progress of pre-trained language models in the biomedical domain and their applications in biomedical downstream tasks. Particularly, we discuss the motivations and propose a taxonomy of existing biomedical PLMs. Their applications in biomedical downstream tasks are exhaustively discussed. At last, we illustrate various limitations and future trends, which we hope can provide inspiration for the future research of the research community.

* 46 pages 

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Are Words the Quanta of Human Language? Extending the Domain of Quantum Cognition

Oct 10, 2021
Diederik Aerts, Lester Beltran

Quantum structures were identified as relevant for describing situations occurring in human cognition in the domain of quantum cognition and were also fruitfully used in information retrieval and natural language processing in the domain of quantum information theory. In the present article, we build on recent prior work and show that additionally to the already identified quantum structures also quantization is present in human cognition. It appears in the form of the words behaving as quanta of human language, very analogous to how photons behave as quanta of electromagnetic radiation. We illustrate this by showing on an example text that Bose-Einstein statistics provides a perfect model while Maxwell-Boltzmann statistics is totally inadequate. Like the indistinguishability of quantum particles introduces a specific form of entanglement this also happens with words. We investigate this entanglement, compute the von Neumann entropy and the amount of non purity of the density matrices of the words and note that non-locality occurs spontaneously. We interpret these results in terms of the prospect of developing a quantum-inspired thermodynamics for the cultural layer of human society, based on a statistical analysis similar to what we propose in this article.

* 28 pages, 3 figures. arXiv admin note: text overlap with arXiv:1909.06845 

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UniMS: A Unified Framework for Multimodal Summarization with Knowledge Distillation

Sep 13, 2021
Zhengkun Zhang, Xiaojun Meng, Yasheng Wang, Xin Jiang, Qun Liu, Zhenglu Yang

With the rapid increase of multimedia data, a large body of literature has emerged to work on multimodal summarization, the majority of which target at refining salient information from textual and visual modalities to output a pictorial summary with the most relevant images. Existing methods mostly focus on either extractive or abstractive summarization and rely on qualified image captions to build image references. We are the first to propose a Unified framework for Multimodal Summarization grounding on BART, UniMS, that integrates extractive and abstractive objectives, as well as selecting the image output. Specially, we adopt knowledge distillation from a vision-language pretrained model to improve image selection, which avoids any requirement on the existence and quality of image captions. Besides, we introduce a visual guided decoder to better integrate textual and visual modalities in guiding abstractive text generation. Results show that our best model achieves a new state-of-the-art result on a large-scale benchmark dataset. The newly involved extractive objective as well as the knowledge distillation technique are proven to bring a noticeable improvement to the multimodal summarization task.


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