pwc-archive/papers-with-abstracts huggingface dataset:
Legal Judgment Prediction with Multi-Stage CaseRepresentation Learning in the Real Court Setting
Authors: Luyao Ma, Yating Zhang, Tianyi Wang, Xiaozhong Liu, Wei Ye, Changlong Sun, Shikun Zhang
Date: 2021-07-12 00:00:00
Tasks: Multi-Task Learning

Legal judgment prediction(LJP) is an essential task for legal AI. While prior methods studied on this topic in a pseudo setting by employing the judge-summarized case narrative as the input to predict the judgment, neglecting critical case life-cycle information in real court setting could threaten the case logic representation quality and prediction correctness. In this paper, we introduce a novel challenging dataset from real courtrooms to predict the legal judgment in a reasonably encyclopedic manner by leveraging the genuine input of the case -- plaintiff's claims and court debate data, from which the case's facts are automatically recognized by comprehensively understanding the multi-role dialogues of the court debate, and then learnt to discriminate the claims so as to reach the final judgment through multi-task learning. An extensive set of experiments with a large civil trial data set shows that the proposed model can more accurately characterize the interactions among claims, fact and debate for legal judgment prediction, achieving significant improvements over strong state-of-the-art baselines. Moreover, the user study conducted with real judges and law school students shows the neural predictions can also be interpretable and easily observed, and thus enhancing the trial efficiency and judgment quality.
Dialogue State Tracking with Multi-Level Fusion of Predicted Dialogue States and Conversations
Authors: Jingyao Zhou, Haipang Wu, Zehao Lin, Guodun Li, Yin Zhang
Date: 2021-07-12 00:00:00
Proceeding: SIGDIAL (ACL) 2021 7
Tasks: Dialogue State Tracking

Most recently proposed approaches in dialogue state tracking (DST) leverage the context and the last dialogue states to track current dialogue states, which are often slot-value pairs. Although the context contains the complete dialogue information, the information is usually indirect and even requires reasoning to obtain. The information in the lastly predicted dialogue states is direct, but when there is a prediction error, the dialogue information from this source will be incomplete or erroneous. In this paper, we propose the Dialogue State Tracking with Multi-Level Fusion of Predicted Dialogue States and Conversations network (FPDSC). This model extracts information of each dialogue turn by modeling interactions among each turn utterance, the corresponding last dialogue states, and dialogue slots. Then the representation of each dialogue turn is aggregated by a hierarchical structure to form the passage information, which is utilized in the current turn of DST. Experimental results validate the effectiveness of the fusion network with 55.03% and 59.07% joint accuracy on MultiWOZ 2.0 and MultiWOZ 2.1 datasets, which reaches the state-of-the-art performance. Furthermore, we conduct the deleted-value and related-slot experiments on MultiWOZ 2.1 to evaluate our model.
Computer-assisted construct classification of organizational performance concerning different stakeholder groups
Authors: Seethalakshmi Gopalakrishnan, Victor Chen, Gus Hahn-Powell, Bharadwaj Tirunagar
Date: 2021-07-11 00:00:00
Tasks: Articles, Classification, Management

The number of research articles in business and management has dramatically increased along with terminology, constructs, and measures. Proper classification of organizational performance constructs from research articles plays an important role in categorizing the literature and understanding to whom its research implications may be relevant. In this work, we classify constructs (i.e., concepts and terminology used to capture different aspects of organizational performance) in research articles into a three-level categorization: (a) performance and non-performance categories (Level 0); (b) for performance constructs, stakeholder group-level of performance concerning investors, customers, employees, and the society (community and natural environment) (Level 1); and (c) for each stakeholder group-level, subcategories of different ways of measurement (Level 2). We observed that increasing contextual information with features extracted from surrounding sentences and external references improves classification of disaggregate-level labels, given limited training data. Our research has implications for computer-assisted construct identification and classification - an essential step for research synthesis.
Multilingual and crosslingual speech recognition using phonological-vector based phone embeddings
Authors: Chengrui Zhu, Keyu An, Huahuan Zheng, Zhijian Ou
Date: 2021-07-11 00:00:00
Tasks: speech-recognition, Speech Recognition

The use of phonological features (PFs) potentially allows language-specific phones to remain linked in training, which is highly desirable for information sharing for multilingual and crosslingual speech recognition methods for low-resourced languages. A drawback suffered by previous methods in using phonological features is that the acoustic-to-PF extraction in a bottom-up way is itself difficult. In this paper, we propose to join phonology driven phone embedding (top-down) and deep neural network (DNN) based acoustic feature extraction (bottom-up) to calculate phone probabilities. The new method is called JoinAP (Joining of Acoustics and Phonology). Remarkably, no inversion from acoustics to phonological features is required for speech recognition. For each phone in the IPA (International Phonetic Alphabet) table, we encode its phonological features to a phonological-vector, and then apply linear or nonlinear transformation of the phonological-vector to obtain the phone embedding. A series of multilingual and crosslingual (both zero-shot and few-shot) speech recognition experiments are conducted on the CommonVoice dataset (German, French, Spanish and Italian) and the AISHLL-1 dataset (Mandarin), and demonstrate the superiority of JoinAP with nonlinear phone embeddings over both JoinAP with linear phone embeddings and the traditional method with flat phone embeddings.
Computational Paremiology: Charting the temporal, ecological dynamics of proverb use in books, news articles, and tweets
Authors: E. Davis, C. M. Danforth, W. Mieder, P. S. Dodds
Date: 2021-07-10 00:00:00
Tasks: Articles, Cultural Vocal Bursts Intensity Prediction

Proverbs are an essential component of language and culture, and though much attention has been paid to their history and currency, there has been comparatively little quantitative work on changes in the frequency with which they are used over time. With wider availability of large corpora reflecting many diverse genres of documents, it is now possible to take a broad and dynamic view of the importance of the proverb. Here, we measure temporal changes in the relevance of proverbs within three corpora, differing in kind, scale, and time frame: Millions of books over centuries; hundreds of millions of news articles over twenty years; and billions of tweets over a decade. We find that proverbs present heavy-tailed frequency-of-usage rank distributions in each venue; exhibit trends reflecting the cultural dynamics of the eras covered; and have evolved into contemporary forms on social media.
PatentMiner: Patent Vacancy Mining via Context-enhanced and Knowledge-guided Graph Attention
Authors: Gaochen Wu, Bin Xu, Yuxin Qin, Fei Kong, Bangchang Liu, Hongwen Zhao, Dejie Chang
Date: 2021-07-10 00:00:00
Tasks: Graph Attention, Link Prediction, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), Prediction

Although there are a small number of work to conduct patent research by building knowledge graph, but without constructing patent knowledge graph using patent documents and combining latest natural language processing methods to mine hidden rich semantic relationships in existing patents and predict new possible patents. In this paper, we propose a new patent vacancy prediction approach named PatentMiner to mine rich semantic knowledge and predict new potential patents based on knowledge graph (KG) and graph attention mechanism. Firstly, patent knowledge graph over time (e.g. year) is constructed by carrying out named entity recognition and relation extrac-tion from patent documents. Secondly, Common Neighbor Method (CNM), Graph Attention Networks (GAT) and Context-enhanced Graph Attention Networks (CGAT) are proposed to perform link prediction in the constructed knowledge graph to dig out the potential triples. Finally, patents are defined on the knowledge graph by means of co-occurrence relationship, that is, each patent is represented as a fully connected subgraph containing all its entities and co-occurrence relationships of the patent in the knowledge graph; Furthermore, we propose a new patent prediction task which predicts a fully connected subgraph with newly added prediction links as a new pa-tent. The experimental results demonstrate that our proposed patent predic-tion approach can correctly predict new patents and Context-enhanced Graph Attention Networks is much better than the baseline. Meanwhile, our proposed patent vacancy prediction task still has significant room to im-prove.
Noise Stability Regularization for Improving BERT Fine-tuning
Authors: Hang Hua, Xingjian Li, Dejing Dou, Cheng-Zhong Xu, Jiebo Luo
Date: 2021-07-10 00:00:00
Proceeding: NAACL 2021 4
Tasks: Sensitivity

Fine-tuning pre-trained language models such as BERT has become a common practice dominating leaderboards across various NLP tasks. Despite its recent success and wide adoption, this process is unstable when there are only a small number of training samples available. The brittleness of this process is often reflected by the sensitivity to random seeds. In this paper, we propose to tackle this problem based on the noise stability property of deep nets, which is investigated in recent literature (Arora et al., 2018; Sanyal et al., 2020). Specifically, we introduce a novel and effective regularization method to improve fine-tuning on NLP tasks, referred to as Layer-wise Noise Stability Regularization (LNSR). We extend the theories about adding noise to the input and prove that our method gives a stabler regularization effect. We provide supportive evidence by experimentally confirming that well-performing models show a low sensitivity to noise and fine-tuning with LNSR exhibits clearly higher generalizability and stability. Furthermore, our method also demonstrates advantages over other state-of-the-art algorithms including L2-SP (Li et al., 2018), Mixout (Lee et al., 2020) and SMART (Jiang et al., 2020).
Assessing Data Efficiency in Task-Oriented Semantic Parsing
Authors: Shrey Desai, Akshat Shrivastava, Justin Rill, Brian Moran, Safiyyah Saleem, Alexander Zotov, Ahmed Aly
Date: 2021-07-10 00:00:00
Tasks: Semantic Parsing

Data efficiency, despite being an attractive characteristic, is often challenging to measure and optimize for in task-oriented semantic parsing; unlike exact match, it can require both model- and domain-specific setups, which have, historically, varied widely across experiments. In our work, as a step towards providing a unified solution to data-efficiency-related questions, we introduce a four-stage protocol which gives an approximate measure of how much in-domain, "target" data a parser requires to achieve a certain quality bar. Specifically, our protocol consists of (1) sampling target subsets of different cardinalities, (2) fine-tuning parsers on each subset, (3) obtaining a smooth curve relating target subset (%) vs. exact match (%), and (4) referencing the curve to mine ad-hoc (target subset, exact match) points. We apply our protocol in two real-world case studies -- model generalizability and intent complexity -- illustrating its flexibility and applicability to practitioners in task-oriented semantic parsing.
An Initial Investigation of Non-Native Spoken Question-Answering
Authors: Vatsal Raina, Mark J. F. Gales
Date: 2021-07-09 00:00:00
Tasks: Question Answering, Reading Comprehension, Transfer Learning

Text-based machine comprehension (MC) systems have a wide-range of applications, and standard corpora exist for developing and evaluating approaches. There has been far less research on spoken question answering (SQA) systems. The SQA task considered in this paper is to extract the answer from a candidate$\text{'}$s spoken response to a question in a prompt-response style language assessment test. Applying these MC approaches to this SQA task rather than, for example, off-topic response detection provides far more detailed information that can be used for further downstream processing. One significant challenge is the lack of appropriately annotated speech corpora to train systems for this task. Hence, a transfer-learning style approach is adopted where a system trained on text-based MC is evaluated on an SQA task with non-native speakers. Mismatches must be considered between text documents and spoken responses; non-native spoken grammar and written grammar. In practical SQA, ASR systems are used, necessitating an investigation of the impact of ASR errors. We show that a simple text-based ELECTRA MC model trained on SQuAD2.0 transfers well for SQA. It is found that there is an approximately linear relationship between ASR errors and the SQA assessment scores but grammar mismatches have minimal impact.
Noisy Training Improves E2E ASR for the Edge
Authors: Dilin Wang, Yuan Shangguan, Haichuan Yang, Pierce Chuang, Jiatong Zhou, Meng Li, Ganesh Venkatesh, Ozlem Kalinli, Vikas Chandra
Date: 2021-07-09 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Data Augmentation, speech-recognition, Speech Recognition

Automatic speech recognition (ASR) has become increasingly ubiquitous on modern edge devices. Past work developed streaming End-to-End (E2E) all-neural speech recognizers that can run compactly on edge devices. However, E2E ASR models are prone to overfitting and have difficulties in generalizing to unseen testing data. Various techniques have been proposed to regularize the training of ASR models, including layer normalization, dropout, spectrum data augmentation and speed distortions in the inputs. In this work, we present a simple yet effective noisy training strategy to further improve the E2E ASR model training. By introducing random noise to the parameter space during training, our method can produce smoother models at convergence that generalize better. We apply noisy training to improve both dense and sparse state-of-the-art Emformer models and observe consistent WER reduction. Specifically, when training Emformers with 90% sparsity, we achieve 12% and 14% WER improvements on the LibriSpeech Test-other and Test-clean data set, respectively.
Can Evolutionary Computation Help us to Crib the Voynich Manuscript ?
Authors: Daniel Devatman Hromada
Date: 2021-07-07 00:00:00

Departing from the postulate that Voynich Manuscript is not a hoax but rather encodes authentic contents, our article presents an evolutionary algorithm which aims to find the most optimal mapping between voynichian glyphs and candidate phonemic values. Core component of the decoding algorithm is a process of maximization of a fitness function which aims to find most optimal set of substitution rules allowing to transcribe the part of the manuscript -- which we call the Calendar -- into lists of feminine names. This leads to sets of character subsitution rules which allow us to consistently transcribe dozens among three hundred calendar tokens into feminine names: a result far surpassing both ``popular'' as well as "state of the art" tentatives to crack the manuscript. What's more, by using name lists stemming from different languages as potential cribs, our ``adaptive'' method can also be useful in identification of the language in which the manuscript is written. As far as we can currently tell, results of our experiments indicate that the Calendar part of the manuscript contains names from baltoslavic, balkanic or hebrew language strata. Two further indications are also given: primo, highest fitness values were obtained when the crib list contains names with specific infixes at token's penultimate position as is the case, for example, for slavic \textbf{feminine diminutives} (i.e. names ending with -ka and not -a). In the most successful scenario, 240 characters contained in 35 distinct Voynichese tokens were successfully transcribed. Secundo, in case of crib stemming from Hebrew language, whole adaptation process converges to significantly better fitness values when transcribing voynichian tokens whose order of individual characters have been reversed, and when lists feminine and not masculine names are used as the crib.
End-to-End Rich Transcription-Style Automatic Speech Recognition with Semi-Supervised Learning
Authors: Tomohiro Tanaka, Ryo Masumura, Mana Ihori, Akihiko Takashima, Shota Orihashi, Naoki Makishima
Date: 2021-07-07 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), speech-recognition, Speech Recognition

We propose a semi-supervised learning method for building end-to-end rich transcription-style automatic speech recognition (RT-ASR) systems from small-scale rich transcription-style and large-scale common transcription-style datasets. In spontaneous speech tasks, various speech phenomena such as fillers, word fragments, laughter and coughs, etc. are often included. While common transcriptions do not give special awareness to these phenomena, rich transcriptions explicitly convert them into special phenomenon tokens as well as textual tokens. In previous studies, the textual and phenomenon tokens were simultaneously estimated in an end-to-end manner. However, it is difficult to build accurate RT-ASR systems because large-scale rich transcription-style datasets are often unavailable. To solve this problem, our training method uses a limited rich transcription-style dataset and common transcription-style dataset simultaneously. The Key process in our semi-supervised learning is to convert the common transcription-style dataset into a pseudo-rich transcription-style dataset. To this end, we introduce style tokens which control phenomenon tokens are generated or not into transformer-based autoregressive modeling. We use this modeling for generating the pseudo-rich transcription-style datasets and for building RT-ASR system from the pseudo and original datasets. Our experiments on spontaneous ASR tasks showed the effectiveness of the proposed method.
Oriental Language Recognition (OLR) 2020: Summary and Analysis
Authors: Jing Li, Binling Wang, Yiming Zhi, Zheng Li, Lin Li, Qingyang Hong, Dong Wang
Date: 2021-07-05 00:00:00
Tasks: Dialect Identification, Language Identification, valid

The fifth Oriental Language Recognition (OLR) Challenge focuses on language recognition in a variety of complex environments to promote its development. The OLR 2020 Challenge includes three tasks: (1) cross-channel language identification, (2) dialect identification, and (3) noisy language identification. We choose Cavg as the principle evaluation metric, and the Equal Error Rate (EER) as the secondary metric. There were 58 teams participating in this challenge and one third of the teams submitted valid results. Compared with the best baseline, the Cavg values of Top 1 system for the three tasks were relatively reduced by 82%, 62% and 48%, respectively. This paper describes the three tasks, the database profile, and the final results. We also outline the novel approaches that improve the performance of language recognition systems most significantly, such as the utilization of auxiliary information.
Self-Referential Quality Diversity Through Differential Map-Elites
Authors: Tae Jong Choi, Julian Togelius
Date: 2021-07-11 00:00:00
Tasks: Diversity

Differential MAP-Elites is a novel algorithm that combines the illumination capacity of CVT-MAP-Elites with the continuous-space optimization capacity of Differential Evolution. The algorithm is motivated by observations that illumination algorithms, and quality-diversity algorithms in general, offer qualitatively new capabilities and applications for evolutionary computation yet are in their original versions relatively unsophisticated optimizers. The basic Differential MAP-Elites algorithm, introduced for the first time here, is relatively simple in that it simply combines the operators from Differential Evolution with the map structure of CVT-MAP-Elites. Experiments based on 25 numerical optimization problems suggest that Differential MAP-Elites clearly outperforms CVT-MAP-Elites, finding better-quality and more diverse solutions.
Um Metodo para Busca Automatica de Redes Neurais Artificiais
Authors: Anderson P. da Silva, Teresa B. Ludermir, Leandro M. Almeida
Date: 2021-07-09 00:00:00

This paper describes a method that automatically searches Artificial Neural Networks using Cellular Genetic Algorithms. The main difference of this method for a common genetic algorithm is the use of a cellular automaton capable of providing the location for individuals, reducing the possibility of local minima in search space. This method employs an evolutionary search for simultaneous choices of initial weights, transfer functions, architectures and learning rules. Experimental results have shown that the developed method can find compact, efficient networks with a satisfactory generalization power and with shorter training times when compared to other methods found in the literature.
Recent advances in Bayesian optimization with applications to parameter reconstruction in optical nano-metrology
Authors: Matthias Plock, Sven Burger, Philipp-Immanuel Schneider
Date: 2021-07-12 00:00:00
Tasks: Bayesian Optimization

Parameter reconstruction is a common problem in optical nano metrology. It generally involves a set of measurements, to which one attempts to fit a numerical model of the measurement process. The model evaluation typically involves to solve Maxwell's equations and is thus time consuming. This makes the reconstruction computationally demanding. Several methods exist for fitting the model to the measurements. On the one hand, Bayesian optimization methods for expensive black-box optimization enable an efficient reconstruction by training a machine learning model of the squared sum of deviations. On the other hand, curve fitting algorithms, such as the Levenberg-Marquardt method, take the deviations between all model outputs and corresponding measurement values into account which enables a fast local convergence. In this paper we present a Bayesian Target Vector Optimization scheme which combines these two approaches. We compare the performance of the presented method against a standard Levenberg-Marquardt-like algorithm, a conventional Bayesian optimization scheme, and the L-BFGS-B and Nelder-Mead simplex algorithms. As a stand-in for problems from nano metrology, we employ a non-linear least-square problem from the NIST Standard Reference Database. We find that the presented method generally uses fewer calls of the model function than any of the competing schemes to achieve similar reconstruction performance.