pwc-archive/papers-with-abstracts huggingface dataset:
CoSQA: 20,000+ Web Queries for Code Search and Question Answering
Authors: JunJie Huang, Duyu Tang, Linjun Shou, Ming Gong, Ke Xu, Daxin Jiang, Ming Zhou, Nan Duan
Date: 2021-05-27 00:00:00
Proceeding: ACL 2021 5
Tasks: Code Search, Contrastive Learning, Natural Language Queries, Question Answering

Finding codes given natural language query isb eneficial to the productivity of software developers. Future progress towards better semantic matching between query and code requires richer supervised training resources. To remedy this, we introduce the CoSQA dataset.It includes 20,604 labels for pairs of natural language queries and codes, each annotated by at least 3 human annotators. We further introduce a contrastive learning method dubbed CoCLR to enhance query-code matching, which works as a data augmenter to bring more artificially generated training instances. We show that evaluated on CodeXGLUE with the same CodeBERT model, training on CoSQA improves the accuracy of code question answering by 5.1%, and incorporating CoCLR brings a further improvement of 10.5%.
TranSmart: A Practical Interactive Machine Translation System
Authors: Guoping Huang, Lemao Liu, Xing Wang, Longyue Wang, Huayang Li, Zhaopeng Tu, Chengyan Huang, Shuming Shi
Date: 2021-05-27 00:00:00
Tasks: Machine Translation, Sentence, Translation

Automatic machine translation is super efficient to produce translations yet their quality is not guaranteed. This technique report introduces TranSmart, a practical human-machine interactive translation system that is able to trade off translation quality and efficiency. Compared to existing publicly available interactive translation systems, TranSmart supports three key features, word-level autocompletion, sentence-level autocompletion and translation memory. By word-level and sentence-level autocompletion, TranSmart allows users to interactively translate words in their own manners rather than the strict manner from left to right. In addition, TranSmart has the potential to avoid similar translation mistakes by using translated sentences in history as its memory. This report presents major functions of TranSmart, algorithms for achieving these functions, how to use the TranSmart APIs, and evaluation results of some key functions. TranSmart is publicly available at its homepage (https://transmart.qq.com).
Extremely low-resource machine translation for closely related languages
Authors: Maali Tars, Andre Tättar, Mark Fišel
Date: 2021-05-27 00:00:00
Proceeding: NoDaLiDa 2021 5
Tasks: Low Resource Neural Machine Translation, Low-Resource Neural Machine Translation, Machine Translation, Transfer Learning, Translation

An effective method to improve extremely low-resource neural machine translation is multilingual training, which can be improved by leveraging monolingual data to create synthetic bilingual corpora using the back-translation method. This work focuses on closely related languages from the Uralic language family: from Estonian and Finnish geographical regions. We find that multilingual learning and synthetic corpora increase the translation quality in every language pair for which we have data. We show that transfer learning and fine-tuning are very effective for doing low-resource machine translation and achieve the best results. We collected new parallel data for V\~oro, North and South Saami and present first results of neural machine translation for these languages.
Finding top performers through email patterns analysis
Authors: Q. Wen, P. A. Gloor, A. Fronzetti Colladon, P. Tickoo, T. Joshi
Date: 2021-05-27 00:00:00

In the information economy, individuals' work performance is closely associated with their digital communication strategies. This study combines social network and semantic analysis to develop a method to identify top performers based on email communication. By reviewing existing literature, we identified the indicators that quantify email communication into measurable dimensions. To empirically examine the predictive power of the proposed indicators, we collected 2 million email archive of 578 executives in an international service company. Panel regression was employed to derive interpretable association between email indicators and top performance. The results suggest that top performers tend to assume central network positions and have high responsiveness to emails. In email contents, top performers use more positive and complex language, with low emotionality, but rich in influential words that are probably reused by co-workers. To better explore the predictive power of the email indicators, we employed AdaBoost machine learning models, which achieved 83.56% accuracy in identifying top performers. With cluster analysis, we further find three categories of top performers, "networkers" with central network positions, "influencers" with influential ideas and "positivists" with positive sentiments. The findings suggest that top performers have distinctive email communication patterns, laying the foundation for grounding email communication competence in theory. The proposed email analysis method also provides a tool to evaluate the different types of individual communication styles.
Adaptive Nearest Neighbor Machine Translation
Authors: Xin Zheng, Zhirui Zhang, Junliang Guo, ShuJian Huang, Boxing Chen, Weihua Luo, Jiajun Chen
Date: 2021-05-27 00:00:00
Proceeding: ACL 2021 5
Tasks: Machine Translation, NMT, Retrieval, Translation

kNN-MT, recently proposed by Khandelwal et al. (2020a), successfully combines pre-trained neural machine translation (NMT) model with token-level k-nearest-neighbor (kNN) retrieval to improve the translation accuracy. However, the traditional kNN algorithm used in kNN-MT simply retrieves a same number of nearest neighbors for each target token, which may cause prediction errors when the retrieved neighbors include noises. In this paper, we propose Adaptive kNN-MT to dynamically determine the number of k for each target token. We achieve this by introducing a light-weight Meta-k Network, which can be efficiently trained with only a few training samples. On four benchmark machine translation datasets, we demonstrate that the proposed method is able to effectively filter out the noises in retrieval results and significantly outperforms the vanilla kNN-MT model. Even more noteworthy is that the Meta-k Network learned on one domain could be directly applied to other domains and obtain consistent improvements, illustrating the generality of our method. Our implementation is open-sourced at https://github.com/zhengxxn/adaptive-knn-mt.
Investigating label suggestions for opinion mining in German Covid-19 social media
Authors: Tilman Beck, Ji-Ung Lee, Christina Viehmann, Marcus Maurer, Oliver Quiring, Iryna Gurevych
Date: 2021-05-27 00:00:00
Proceeding: ACL 2021 5
Tasks: Opinion Mining, Transfer Learning

This work investigates the use of interactively updated label suggestions to improve upon the efficiency of gathering annotations on the task of opinion mining in German Covid-19 social media data. We develop guidelines to conduct a controlled annotation study with social science students and find that suggestions from a model trained on a small, expert-annotated dataset already lead to a substantial improvement - in terms of inter-annotator agreement(+.14 Fleiss' $\kappa$) and annotation quality - compared to students that do not receive any label suggestions. We further find that label suggestions from interactively trained models do not lead to an improvement over suggestions from a static model. Nonetheless, our analysis of suggestion bias shows that annotators remain capable of reflecting upon the suggested label in general. Finally, we confirm the quality of the annotated data in transfer learning experiments between different annotator groups. To facilitate further research in opinion mining on social media data, we release our collected data consisting of 200 expert and 2,785 student annotations.
Improve Query Focused Abstractive Summarization by Incorporating Answer Relevance
Authors: Dan Su, Tiezheng Yu, Pascale Fung
Date: 2021-05-27 00:00:00
Proceeding: Findings (ACL) 2021 8
Tasks: Abstractive Text Summarization, Query-focused Summarization, Question Answering

Query focused summarization (QFS) models aim to generate summaries from source documents that can answer the given query. Most previous work on QFS only considers the query relevance criterion when producing the summary. However, studying the effect of answer relevance in the summary generating process is also important. In this paper, we propose QFS-BART, a model that incorporates the explicit answer relevance of the source documents given the query via a question answering model, to generate coherent and answer-related summaries. Furthermore, our model can take advantage of large pre-trained models which improve the summarization performance significantly. Empirical results on the Debatepedia dataset show that the proposed model achieves the new state-of-the-art performance.
Corpus-Level Evaluation for Event QA: The IndiaPoliceEvents Corpus Covering the 2002 Gujarat Violence
Authors: Andrew Halterman, Katherine A. Keith, Sheikh Muhammad Sarwar, Brendan O'Connor
Date: 2021-05-27 00:00:00
Proceeding: Findings (ACL) 2021 8
Tasks: Articles, Document Ranking, Event Extraction, Natural Language Inference, Natural Questions, Passage Retrieval, Retrieval, Sentence, Sentence Classification

Automated event extraction in social science applications often requires corpus-level evaluations: for example, aggregating text predictions across metadata and unbiased estimates of recall. We combine corpus-level evaluation requirements with a real-world, social science setting and introduce the IndiaPoliceEvents corpus--all 21,391 sentences from 1,257 English-language Times of India articles about events in the state of Gujarat during March 2002. Our trained annotators read and label every document for mentions of police activity events, allowing for unbiased recall evaluations. In contrast to other datasets with structured event representations, we gather annotations by posing natural questions, and evaluate off-the-shelf models for three different tasks: sentence classification, document ranking, and temporal aggregation of target events. We present baseline results from zero-shot BERT-based models fine-tuned on natural language inference and passage retrieval tasks. Our novel corpus-level evaluations and annotation approach can guide creation of similar social-science-oriented resources in the future.
Contrastive Fine-tuning Improves Robustness for Neural Rankers
Authors: Xiaofei Ma, Cicero Nogueira dos santos, Andrew O. Arnold
Date: 2021-05-27 00:00:00
Proceeding: Findings (ACL) 2021 8
Tasks: Data Augmentation, Passage Ranking

The performance of state-of-the-art neural rankers can deteriorate substantially when exposed to noisy inputs or applied to a new domain. In this paper, we present a novel method for fine-tuning neural rankers that can significantly improve their robustness to out-of-domain data and query perturbations. Specifically, a contrastive loss that compares data points in the representation space is combined with the standard ranking loss during fine-tuning. We use relevance labels to denote similar/dissimilar pairs, which allows the model to learn the underlying matching semantics across different query-document pairs and leads to improved robustness. In experiments with four passage ranking datasets, the proposed contrastive fine-tuning method obtains improvements on robustness to query reformulations, noise perturbations, and zero-shot transfer for both BERT and BART based rankers. Additionally, our experiments show that contrastive fine-tuning outperforms data augmentation for robustifying neural rankers.
Directed Acyclic Graph Network for Conversational Emotion Recognition
Authors: Weizhou Shen, Siyue Wu, Yunyi Yang, Xiaojun Quan
Date: 2021-05-27 00:00:00
Proceeding: ACL 2021 5
Tasks: Emotion Recognition, Emotion Recognition in Conversation

The modeling of conversational context plays a vital role in emotion recognition from conversation (ERC). In this paper, we put forward a novel idea of encoding the utterances with a directed acyclic graph (DAG) to better model the intrinsic structure within a conversation, and design a directed acyclic neural network, namely DAG-ERC, to implement this idea. In an attempt to combine the strengths of conventional graph-based neural models and recurrence-based neural models, DAG-ERC provides a more intuitive way to model the information flow between long-distance conversation background and nearby context. Extensive experiments are conducted on four ERC benchmarks with state-of-the-art models employed as baselines for comparison. The empirical results demonstrate the superiority of this new model and confirm the motivation of the directed acyclic graph architecture for ERC.
How Does Distilled Data Complexity Impact the Quality and Confidence of Non-Autoregressive Machine Translation?
Authors: Weijia Xu, Shuming Ma, Dongdong Zhang, Marine Carpuat
Date: 2021-05-27 00:00:00
Proceeding: Findings (ACL) 2021 8
Tasks: Diversity, Knowledge Distillation, Machine Translation, Translation

While non-autoregressive (NAR) models are showing great promise for machine translation, their use is limited by their dependence on knowledge distillation from autoregressive models. To address this issue, we seek to understand why distillation is so effective. Prior work suggests that distilled training data is less complex than manual translations. Based on experiments with the Levenshtein Transformer and the Mask-Predict NAR models on the WMT14 German-English task, this paper shows that different types of complexity have different impacts: while reducing lexical diversity and decreasing reordering complexity both help NAR learn better alignment between source and target, and thus improve translation quality, lexical diversity is the main reason why distillation increases model confidence, which affects the calibration of different NAR models differently.
BERTifying the Hidden Markov Model for Multi-Source Weakly Supervised Named Entity Recognition
Authors: Yinghao Li, Pranav Shetty, Lucas Liu, Chao Zhang, Le Song
Date: 2021-05-26 00:00:00
Proceeding: ACL 2021 5
Tasks: named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER, Weakly-Supervised Named Entity Recognition

We study the problem of learning a named entity recognition (NER) tagger using noisy labels from multiple weak supervision sources. Though cheap to obtain, the labels from weak supervision sources are often incomplete, inaccurate, and contradictory, making it difficult to learn an accurate NER model. To address this challenge, we propose a conditional hidden Markov model (CHMM), which can effectively infer true labels from multi-source noisy labels in an unsupervised way. CHMM enhances the classic hidden Markov model with the contextual representation power of pre-trained language models. Specifically, CHMM learns token-wise transition and emission probabilities from the BERT embeddings of the input tokens to infer the latent true labels from noisy observations. We further refine CHMM with an alternate-training approach (CHMM-ALT). It fine-tunes a BERT-NER model with the labels inferred by CHMM, and this BERT-NER's output is regarded as an additional weak source to train the CHMM in return. Experiments on four NER benchmarks from various domains show that our method outperforms state-of-the-art weakly supervised NER models by wide margins.
TexRel: a Green Family of Datasets for Emergent Communications on Relations
Authors: Hugh Perkins
Date: 2021-05-26 00:00:00
Tasks: Clustering, Emergent communications on relations

We propose a new dataset TexRel as a playground for the study of emergent communications, in particular for relations. By comparison with other relations datasets, TexRel provides rapid training and experimentation, whilst being sufficiently large to avoid overfitting in the context of emergent communications. By comparison with using symbolic inputs, TexRel provides a more realistic alternative whilst remaining efficient and fast to learn. We compare the performance of TexRel with a related relations dataset Shapeworld. We provide baseline performance results on TexRel for sender architectures, receiver architectures and end-to-end architectures. We examine the effect of multitask learning in the context of shapes, colors and relations on accuracy, topological similarity and clustering precision. We investigate whether increasing the size of the latent meaning space improves metrics of compositionality. We carry out a case-study on using TexRel to reproduce the results of an experiment in a recent paper that used symbolic inputs, but using our own non-symbolic inputs, from TexRel, instead.
Quantifying and Avoiding Unfair Qualification Labour in Crowdsourcing
Authors: Jonathan K. Kummerfeld
Date: 2021-05-26 00:00:00
Proceeding: ACL 2021 5

Extensive work has argued in favour of paying crowd workers a wage that is at least equivalent to the U.S. federal minimum wage. Meanwhile, research on collecting high quality annotations suggests using a qualification that requires workers to have previously completed a certain number of tasks. If most requesters who pay fairly require workers to have completed a large number of tasks already then workers need to complete a substantial amount of poorly paid work before they can earn a fair wage. Through analysis of worker discussions and guidance for researchers, we estimate that workers spend approximately 2.25 months of full time effort on poorly paid tasks in order to get the qualifications needed for better paid tasks. We discuss alternatives to this qualification and conduct a study of the correlation between qualifications and work quality on two NLP tasks. We find that it is possible to reduce the burden on workers while still collecting high quality data.
Robotic Brain Storm Optimization: A Multi-target Collaborative Searching Paradigm for Swarm Robotics
Authors: Jian Yang, Yuhui Shi
Date: 2021-05-27 00:00:00
Tasks: Clustering

Swarm intelligence optimization algorithms can be adopted in swarm robotics for target searching tasks in a 2-D or 3-D space by treating the target signal strength as fitness values. Many current works in the literature have achieved good performance in single-target search problems. However, when there are multiple targets in an environment to be searched, many swarm intelligence-based methods may converge to specific locations prematurely, making it impossible to explore the environment further. The Brain Storm Optimization (BSO) algorithm imitates a group of humans in solving problems collectively. A series of guided searches can finally obtain a relatively optimal solution for particular optimization problems. Furthermore, with a suitable clustering operation, it has better multi-modal optimization performance, i.e., it can find multiple optima in the objective space. By matching the members in a robotic swarm to the individuals in the algorithm under both environments and robots constraints, this paper proposes a BSO-based collaborative searching framework for swarm robotics called Robotic BSO. The simulation results show that the proposed method can simulate the BSO's guided search characteristics and has an excellent prospect for multi-target searching problems for swarm robotics.
Attention-oriented Brain Storm Optimization for Multimodal Optimization Problems
Authors: Jian Yang, Yuhui Shi
Date: 2021-05-27 00:00:00
Tasks: Clustering

Population-based methods are often used to solve multimodal optimization problems. By combining niching or clustering strategy, the state-of-the-art approaches generally divide the population into several subpopulations to find multiple solutions for a problem at hand. However, these methods only guided by the fitness value during iterations, which are suffering from determining the number of subpopulations, i.e., the number of niche areas or clusters. To compensate for this drawback, this paper presents an Attention-oriented Brain Storm Optimization (ABSO) method that introduces the attention mechanism into a relatively new swarm intelligence algorithm, i.e., Brain Storm Optimization (BSO). By converting the objective space from the fitness space into "attention" space, the individuals are clustered and updated iteratively according to their salient values. Rather than converge to a single global optimum, the proposed method can guide the search procedure to converge to multiple "salient" solutions. The preliminary results show that the proposed method can locate multiple global and local optimal solutions of several multimodal benchmark functions. The proposed method needs less prior knowledge of the problem and can automatically converge to multiple optimums guided by the attention mechanism, which has excellent potential for further development.
BackEISNN: A Deep Spiking Neural Network with Adaptive Self-Feedback and Balanced Excitatory-Inhibitory Neurons
Authors: Dongcheng Zhao, Yi Zeng, Yang Li
Date: 2021-05-27 00:00:00

Spiking neural networks (SNNs) transmit information through discrete spikes, which performs well in processing spatial-temporal information. Due to the non-differentiable characteristic, there still exist difficulties in designing well-performed SNNs. Recently, SNNs trained with backpropagation have shown superior performance due to the proposal of the gradient approximation. However, the performance on complex tasks is still far away from the deep neural networks. Taking inspiration from the autapse in the brain which connects the spiking neurons with a self-feedback connection, we apply an adaptive time-delayed self-feedback on the membrane potential to regulate the spike precisions. As well as, we apply the balanced excitatory and inhibitory neurons mechanism to control the spiking neurons' output dynamically. With the combination of the two mechanisms, we propose a deep spiking neural network with adaptive self-feedback and balanced excitatory and inhibitory neurons (BackEISNN). The experimental results on several standard datasets have shown that the two modules not only accelerate the convergence of the network but also improve the accuracy. For the MNIST, FashionMNIST, and N-MNIST datasets, our model has achieved state-of-the-art performance. For the CIFAR10 dataset, our BackEISNN also gets remarkable performance on a relatively light structure that competes against state-of-the-art SNNs.
On the Exploitation of Neuroevolutionary Information: Analyzing the Past for a More Efficient Future
Authors: Unai Garciarena, Nuno Lourenço, Penousal Machado, Roberto Santana, Alexander Mendiburu
Date: 2021-05-26 00:00:00

Neuroevolutionary algorithms, automatic searches of neural network structures by means of evolutionary techniques, are computationally costly procedures. In spite of this, due to the great performance provided by the architectures which are found, these methods are widely applied. The final outcome of neuroevolutionary processes is the best structure found during the search, and the rest of the procedure is commonly omitted in the literature. However, a good amount of residual information consisting of valuable knowledge that can be extracted is also produced during these searches. In this paper, we propose an approach that extracts this information from neuroevolutionary runs, and use it to build a metamodel that could positively impact future neural architecture searches. More specifically, by inspecting the best structures found during neuroevolutionary searches of generative adversarial networks with varying characteristics (e.g., based on dense or convolutional layers), we propose a Bayesian network-based model which can be used to either find strong neural structures right away, conveniently initialize different structural searches for different problems, or help future optimization of structures of any type to keep finding increasingly better structures where uninformed methods get stuck into local optima.