pwc-archive/papers-with-abstracts huggingface dataset:
DeepKE: A Deep Learning Based Knowledge Extraction Toolkit for Knowledge Base Population
Authors: Ningyu Zhang, Xin Xu, Liankuan Tao, Haiyang Yu, Hongbin Ye, Shuofei Qiao, Xin Xie, Xiang Chen, Zhoubo Li, Lei LI, Xiaozhuan Liang, Yunzhi Yao, Shumin Deng, Peng Wang, Wen Zhang, Zhenru Zhang, Chuanqi Tan, Qiang Chen, Feiyu Xiong, Fei Huang, Guozhou Zheng, Huajun Chen
Date: 2022-01-10 00:00:00
Tasks: Attribute, Attribute Extraction, Cross-Domain Named Entity Recognition, Knowledge Base Population, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), Relation Extraction

We present an open-source and extensible knowledge extraction toolkit DeepKE, supporting complicated low-resource, document-level and multimodal scenarios in the knowledge base population. DeepKE implements various information extraction tasks, including named entity recognition, relation extraction and attribute extraction. With a unified framework, DeepKE allows developers and researchers to customize datasets and models to extract information from unstructured data according to their requirements. Specifically, DeepKE not only provides various functional modules and model implementation for different tasks and scenarios but also organizes all components by consistent frameworks to maintain sufficient modularity and extensibility. We release the source code at GitHub in https://github.com/zjunlp/DeepKE with Google Colab tutorials and comprehensive documents for beginners. Besides, we present an online system in http://deepke.openkg.cn/EN/re_doc_show.html for real-time extraction of various tasks, and a demo video.
Latency Adjustable Transformer Encoder for Language Understanding
Authors: Sajjad Kachuee, Mohammad Sharifkhani
Date: 2022-01-10 00:00:00
Tasks: Natural Language Understanding, Sentiment Analysis, Text Generation

Adjusting the latency, power, and accuracy of natural language understanding models is a desirable objective of an efficient architecture. This paper proposes an efficient Transformer architecture that adjusts the inference computational cost adaptively with a desired inference latency speedup. In fine-tuning phase, the proposed method detects less important hidden sequence elements (word-vectors) and eliminates them in each encoder layer using a proposed Attention Context Contribution (ACC) metric. After the fine-tuning phase, with the novel offline-tuning property, the inference latency of the model can be adjusted in a wide range of inference speedup selections without any further training. The proposed method is applied to the BERT_base, GPT-2 and Flan-T5 models for evaluation. Extensive experiments show that most of the word-vectors in higher Transformer layers have less contribution to the subsequent layers; hence, they can be eliminated to improve the inference latency. Experimental results on extensive sentiment analysis, classification, text generation tasks and regression benchmarks like GLUE showed that the method is effective in various datasets with minimal impact on the input's global context. The method was also evaluated under the instruction tuning paradigm, and its performance was measured using different types of prompting. The proposed method mathematically and experimentally improves the inference latency of BERT_base and GPT-2 by up to 4.8 and 3.72 times with less than 0.75% accuracy drop and passable perplexity on average. The suggested approach posits that in Large Language Models (LLMs), although the complete network is necessary for training, it can be truncated during the fine-tuning phase.
A Planck Radiation and Quantization Scheme for Human Cognition and Language
Authors: Diederik Aerts, Lester Beltran
Date: 2022-01-10 00:00:00
Tasks: Quantization

As a result of the identification of 'identity' and 'indistinguishability' and strong experimental evidence for the presence of the associated Bose-Einstein statistics in human cognition and language, we argued in previous work for an extension of the research domain of quantum cognition. In addition to quantum complex vector spaces and quantum probability models, we showed that quantization itself, with words as quanta, is relevant and potentially important to human cognition. In the present work, we build on this result, and introduce a powerful radiation quantization scheme for human cognition. We show that the lack of independence of the Bose-Einstein statistics compared to the Maxwell-Boltzmann statistics can be explained by the presence of a 'meaning dynamics', which causes words to be attracted to the same words. And so words clump together in the same states, a phenomenon well known for photons in the early years of quantum mechanics, leading to fierce disagreements between Planck and Einstein. Using a simple example, we introduce all the elements to get a better and detailed view of this 'meaning dynamics', such as micro and macro states, and Maxwell-Boltzmann, Bose-Einstein and Fermi-Dirac numbers and weights, and compare this example and its graphs, with the radiation quantization scheme of a Winnie the Pooh story, also with its graphs. By connecting a concept directly to human experience, we show that entanglement is a necessity for preserving the 'meaning dynamics' we identified, and it becomes clear in what way Fermi-Dirac addresses human memory. Within the human mind, as a crucial aspect of memory, in spaces with internal parameters, identical words can nevertheless be assigned different states and hence realize locally and contextually the necessary distinctiveness, structured by a Pauli exclusion principle, for human thought to thrive.
There is no rose without a thorn: Finding weaknesses on BlenderBot 2.0 in terms of Model, Data and User-Centric Approach
Authors: Jungseob Lee, Midan Shim, Suhyune Son, Chanjun Park, Yujin Kim, Heuiseok Lim
Date: 2022-01-10 00:00:00

BlenderBot 2.0 is a dialogue model that represents open-domain chatbots by reflecting real-time information and remembering user information for an extended period using an internet search module and multi-session. Nonetheless, the model still has room for improvement. To this end, we examine BlenderBot 2.0 limitations and errors from three perspectives: model, data, and user. From the data point of view, we highlight the unclear guidelines provided to workers during the crowdsourcing process, as well as a lack of a process for refining hate speech in the collected data and verifying the accuracy of internet-based information. From a user perspective, we identify nine types of limitations of BlenderBot 2.0, and their causes are thoroughly investigated. Furthermore, for each point of view, we propose practical improvement methods and discuss several potential future research directions.
Writing Style Aware Document-level Event Extraction
Authors: Zhuo Xu, Yue Wang, Lu Bai, Lixin Cui
Date: 2022-01-10 00:00:00
Tasks: Document-level Event Extraction, Event Extraction, Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION

Event extraction, the technology that aims to automatically get the structural information from documents, has attracted more and more attention in many fields. Most existing works discuss this issue with the token-level multi-label classification framework by distinguishing the tokens as different roles while ignoring the writing styles of documents. The writing style is a special way of content organizing for documents and it is relative fixed in documents with a special field (e.g. financial, medical documents, etc.). We argue that the writing style contains important clues for judging the roles for tokens and the ignorance of such patterns might lead to the performance degradation for the existing works. To this end, we model the writing style in documents as a distribution of argument roles, i.e., Role-Rank Distribution, and propose an event extraction model with the Role-Rank Distribution based Supervision Mechanism to capture this pattern through the supervised training process of an event extraction task. We compare our model with state-of-the-art methods on several real-world datasets. The empirical results show that our approach outperforms other alternatives with the captured patterns. This verifies the writing style contains valuable information that could improve the performance of the event extraction task.
Style, Content, and the Success of Ideas
Authors: Reihane Boghrati, Jonah Berger, Grant Packard
Date: 2022-01-10 00:00:00
Tasks: Articles

Why do some things succeed in the marketplace of ideas? While some argue that content drives success, others suggest that style, or the way ideas are presented, also plays an important role. To provide a stringent test of style's importance, we examine it in a context where content should be paramount: academic research. While scientists often see writing as a disinterested way to communicate unobstructed truth, a multi-method investigation indicates that writing style shapes impact. Separating style from content can be difficult as papers that tend to use certain language may also write about certain topics. Consequently, we focus on a unique class of words linked to style (i.e., function words such as "and," "the," and "on") that are completely devoid of content. Natural language processing of almost 30,000 articles from a range of disciplines finds that function words explain 13-27% of language's impact on citations. Ancillary analyses explore specific categories of function words to suggest how style matters, highlighting the role of writing simplicity, personal voice, and temporal perspective. Experiments further underscore the causal impact of style. The results suggest how to boost communication's impact and highlight the value of natural language processing for understanding the success of ideas.
Quantifying Gender Bias in Consumer Culture
Authors: Reihane Boghrati, Jonah Berger
Date: 2022-01-10 00:00:00
Tasks: Cultural Vocal Bursts Intensity Prediction

Cultural items like songs have an important impact in creating and reinforcing stereotypes, biases, and discrimination. But the actual nature of such items is often less transparent. Take songs, for example. Are lyrics biased against women? And how have any such biases changed over time? Natural language processing of a quarter of a million songs over 50 years quantifies misogyny. Women are less likely to be associated with desirable traits (i.e., competence), and while this bias has decreased, it persists. Ancillary analyses further suggest that song lyrics may help drive shifts in societal stereotypes towards women, and that lyrical shifts are driven by male artists (as female artists were less biased to begin with). Overall, these results shed light on cultural evolution, subtle measures of bias and discrimination, and how natural language processing and machine learning can provide deeper insight into stereotypes and cultural change.
Projection: A Mixed-Initiative Research Process
Authors: Austin Silveria
Date: 2022-01-09 00:00:00
Tasks: Clustering, Recommendation Systems

Communication of dense information between humans and machines is relatively low bandwidth. Many modern search and recommender systems operate as machine learning black boxes, giving little insight as to how they represent information or why they take certain actions. We present Projection, a mixed-initiative interface that aims to increase the bandwidth of communication between humans and machines throughout the research process. The interface supports adding context to searches and visualizing information in multiple dimensions with techniques such as hierarchical clustering and spatial projections. Potential customers have shown interest in the application integrating their research outlining and search processes, enabling them to structure their searches in hierarchies, and helping them visualize related spaces of knowledge.
An Ensemble Approach to Acronym Extraction using Transformers
Authors: Prashant Sharma, Hadeel Saadany, Leonardo Zilio, Diptesh Kanojia, Constantin Orăsan
Date: 2022-01-09 00:00:00
Tasks: Information Retrieval, Language Modeling, Language Modelling, Machine Translation, Retrieval

Acronyms are abbreviated units of a phrase constructed by using initial components of the phrase in a text. Automatic extraction of acronyms from a text can help various Natural Language Processing tasks like machine translation, information retrieval, and text summarisation. This paper discusses an ensemble approach for the task of Acronym Extraction, which utilises two different methods to extract acronyms and their corresponding long forms. The first method utilises a multilingual contextual language model and fine-tunes the model to perform the task. The second method relies on a convolutional neural network architecture to extract acronyms and append them to the output of the previous method. We also augment the official training dataset with additional training samples extracted from several open-access journals to help improve the task performance. Our dataset analysis also highlights the noise within the current task dataset. Our approach achieves the following macro-F1 scores on test data released with the task: Danish (0.74), English-Legal (0.72), English-Scientific (0.73), French (0.63), Persian (0.57), Spanish (0.65), Vietnamese (0.65). We release our code and models publicly.
Zero-Shot and Few-Shot Classification of Biomedical Articles in Context of the COVID-19 Pandemic
Authors: Simon Lupart, Benoit Favre, Vassilina Nikoulina, Salah Ait-Mokhtar
Date: 2022-01-09 00:00:00
Tasks: Articles, Multi-Task Learning, valid, zero-shot-classification, Zero-Shot Learning

MeSH (Medical Subject Headings) is a large thesaurus created by the National Library of Medicine and used for fine-grained indexing of publications in the biomedical domain. In the context of the COVID-19 pandemic, MeSH descriptors have emerged in relation to articles published on the corresponding topic. Zero-shot classification is an adequate response for timely labeling of the stream of papers with MeSH categories. In this work, we hypothesise that rich semantic information available in MeSH has potential to improve BioBERT representations and make them more suitable for zero-shot/few-shot tasks. We frame the problem as determining if MeSH term definitions, concatenated with paper abstracts are valid instances or not, and leverage multi-task learning to induce the MeSH hierarchy in the representations thanks to a seq2seq task. Results establish a baseline on the MedLine and LitCovid datasets, and probing shows that the resulting representations convey the hierarchical relations present in MeSH.
Indian Language Wordnets and their Linkages with Princeton WordNet
Authors: Diptesh Kanojia, Kevin Patel, Pushpak Bhattacharyya
Date: 2022-01-09 00:00:00
Proceeding: LREC 2018 5

Wordnets are rich lexico-semantic resources. Linked wordnets are extensions of wordnets, which link similar concepts in wordnets of different languages. Such resources are extremely useful in many Natural Language Processing (NLP) applications, primarily those based on knowledge-based approaches. In such approaches, these resources are considered as gold standard/oracle. Thus, it is crucial that these resources hold correct information. Thereby, they are created by human experts. However, human experts in multiple languages are hard to come by. Thus, the community would benefit from sharing of such manually created resources. In this paper, we release mappings of 18 Indian language wordnets linked with Princeton WordNet. We believe that availability of such resources will have a direct impact on the progress in NLP for these languages.
Effect of Toxic Review Content on Overall Product Sentiment
Authors: Mayukh Mukhopadhyay, Sangeeta Sahney
Date: 2022-01-08 00:00:00
Tasks: Sentence

Toxic contents in online product review are a common phenomenon. A content is perceived to be toxic when it is rude, disrespectful, or unreasonable and make individuals leave the discussion. Machine learning algorithms helps the sell side community to identify such toxic patterns and eventually moderate such inputs. Yet, the extant literature provides fewer information about the sentiment of a prospective consumer on the perception of a product after being exposed to such toxic review content. In this study, we collect a balanced data set of review comments from 18 different players segregated into three different sectors from google play-store. Then we calculate the sentence-level sentiment and toxicity score of individual review content. Finally, we use structural equation modelling to quantitatively study the influence of toxic content on overall product sentiment. We observe that comment toxicity negatively influences overall product sentiment but do not exhibit a mediating effect over reviewer score to influence sector-wise relative rating.
Coherence-Based Distributed Document Representation Learning for Scientific Documents
Authors: Shicheng Tan, Shu Zhao, Yanping Zhang
Date: 2022-01-08 00:00:00
Tasks: Information Retrieval, Representation Learning, Retrieval

Distributed document representation is one of the basic problems in natural language processing. Currently distributed document representation methods mainly consider the context information of words or sentences. These methods do not take into account the coherence of the document as a whole, e.g., a relation between the paper title and abstract, headline and description, or adjacent bodies in the document. The coherence shows whether a document is meaningful, both logically and syntactically, especially in scientific documents (papers or patents, etc.). In this paper, we propose a coupled text pair embedding (CTPE) model to learn the representation of scientific documents, which maintains the coherence of the document with coupled text pairs formed by segmenting the document. First, we divide the document into two parts (e.g., title and abstract, etc) which construct a coupled text pair. Then, we adopt negative sampling to construct uncoupled text pairs whose two parts are from different documents. Finally, we train the model to judge whether the text pair is coupled or uncoupled and use the obtained embedding of coupled text pairs as the embedding of documents. We perform experiments on three datasets for one information retrieval task and two recommendation tasks. The experimental results verify the effectiveness of the proposed CTPE model.
A Unified Review of Deep Learning for Automated Medical Coding
Authors: Shaoxiong Ji, Wei Sun, Xiaobo Li, Hang Dong, Ara Taalas, Yijia Zhang, Honghan Wu, Esa Pitkänen, Pekka Marttinen
Date: 2022-01-08 00:00:00
Tasks: Decoder, Deep Learning

Automated medical coding, an essential task for healthcare operation and delivery, makes unstructured data manageable by predicting medical codes from clinical documents. Recent advances in deep learning and natural language processing have been widely applied to this task. However, deep learning-based medical coding lacks a unified view of the design of neural network architectures. This review proposes a unified framework to provide a general understanding of the building blocks of medical coding models and summarizes recent advanced models under the proposed framework. Our unified framework decomposes medical coding into four main components, i.e., encoder modules for text feature extraction, mechanisms for building deep encoder architectures, decoder modules for transforming hidden representations into medical codes, and the usage of auxiliary information. Finally, we introduce the benchmarks and real-world usage and discuss key research challenges and future directions.
Defining maximum acceptable latency of AI-enhanced CAI tools
Authors: claudio Fantinuoli, Maddalena Montecchio
Date: 2022-01-08 00:00:00

Recent years have seen an increasing number of studies around the design of computer-assisted interpreting tools with integrated automatic speech processing and their use by trainees and professional interpreters. This paper discusses the role of system latency of such tools and presents the results of an experiment designed to investigate the maximum system latency that is cognitively acceptable for interpreters working in the simultaneous modality. The results show that interpreters can cope with a system latency of 3 seconds without any major impact in the rendition of the original text, both in terms of accuracy and fluency. This value is above the typical latency of available AI-based CAI tools and paves the way to experiment with larger context-based language models and higher latencies.
Low-Rank Constraints for Fast Inference in Structured Models
Authors: Justin T. Chiu, Yuntian Deng, Alexander M. Rush
Date: 2022-01-08 00:00:00
Proceeding: NeurIPS 2021 12
Tasks: Language Modeling, Language Modelling, Music Modeling

Structured distributions, i.e. distributions over combinatorial spaces, are commonly used to learn latent probabilistic representations from observed data. However, scaling these models is bottlenecked by the high computational and memory complexity with respect to the size of the latent representations. Common models such as Hidden Markov Models (HMMs) and Probabilistic Context-Free Grammars (PCFGs) require time and space quadratic and cubic in the number of hidden states respectively. This work demonstrates a simple approach to reduce the computational and memory complexity of a large class of structured models. We show that by viewing the central inference step as a matrix-vector product and using a low-rank constraint, we can trade off model expressivity and speed via the rank. Experiments with neural parameterized structured models for language modeling, polyphonic music modeling, unsupervised grammar induction, and video modeling show that our approach matches the accuracy of standard models at large state spaces while providing practical speedups.
A New Amharic Speech Emotion Dataset and Classification Benchmark
Authors: Ephrem A. Retta, Eiad Almekhlafi, Richard Sutcliffe, Mustafa Mhamed, Haider Ali, Jun Feng
Date: 2022-01-07 00:00:00
Tasks: Classification, Emotion Recognition, Sentence, Speech Emotion Recognition

In this paper we present the Amharic Speech Emotion Dataset (ASED), which covers four dialects (Gojjam, Wollo, Shewa and Gonder) and five different emotions (neutral, fearful, happy, sad and angry). We believe it is the first Speech Emotion Recognition (SER) dataset for the Amharic language. 65 volunteer participants, all native speakers, recorded 2,474 sound samples, two to four seconds in length. Eight judges assigned emotions to the samples with high agreement level (Fleiss kappa = 0.8). The resulting dataset is freely available for download. Next, we developed a four-layer variant of the well-known VGG model which we call VGGb. Three experiments were then carried out using VGGb for SER, using ASED. First, we investigated whether Mel-spectrogram features or Mel-frequency Cepstral coefficient (MFCC) features work best for Amharic. This was done by training two VGGb SER models on ASED, one using Mel-spectrograms and the other using MFCC. Four forms of training were tried, standard cross-validation, and three variants based on sentences, dialects and speaker groups. Thus, a sentence used for training would not be used for testing, and the same for a dialect and speaker group. The conclusion was that MFCC features are superior under all four training schemes. MFCC was therefore adopted for Experiment 2, where VGGb and three other existing models were compared on ASED: RESNet50, Alex-Net and LSTM. VGGb was found to have very good accuracy (90.73%) as well as the fastest training time. In Experiment 3, the performance of VGGb was compared when trained on two existing SER datasets, RAVDESS (English) and EMO-DB (German) as well as on ASED (Amharic). Results are comparable across these languages, with ASED being the highest. This suggests that VGGb can be successfully applied to other languages. We hope that ASED will encourage researchers to experiment with other models for Amharic SER.