pwc-archive/papers-with-abstracts huggingface dataset:
A Summary of the ALQAC 2021 Competition
Authors: Nguyen Ha Thanh, Bui Minh Quan, Chau Nguyen, Tung Le, Nguyen Minh Phuong, Dang Tran Binh, Vuong Thi Hai Yen, Teeradaj Racharak, Nguyen Le Minh, Tran Duc Vu, Phan Viet Anh, Nguyen Truong Son, Huy Tien Nguyen, Bhumindr Butr-indr, Peerapon Vateekul, Prachya Boonkwan
Date: 2022-04-22 00:00:00
Tasks: Information Retrieval, Question Answering, Retrieval, Task 2

We summarize the evaluation of the first Automated Legal Question Answering Competition (ALQAC 2021). The competition this year contains three tasks, which aims at processing the statute law document, which are Legal Text Information Retrieval (Task 1), Legal Text Entailment Prediction (Task 2), and Legal Text Question Answering (Task 3). The final goal of these tasks is to build a system that can automatically determine whether a particular statement is lawful. There is no limit to the approaches of the participating teams. This year, there are 5 teams participating in Task 1, 6 teams participating in Task 2, and 5 teams participating in Task 3. There are in total 36 runs submitted to the organizer. In this paper, we summarize each team's approaches, official results, and some discussion about the competition. Only results of the teams who successfully submit their approach description paper are reported in this paper.
Identifying Chinese Opinion Expressions with Extremely-Noisy Crowdsourcing Annotations
Authors: Xin Zhang, Guangwei Xu, Yueheng Sun, Meishan Zhang, Xiaobin Wang, Min Zhang
Date: 2022-04-22 00:00:00
Proceeding: ACL 2022 5

Recent works of opinion expression identification (OEI) rely heavily on the quality and scale of the manually-constructed training corpus, which could be extremely difficult to satisfy. Crowdsourcing is one practical solution for this problem, aiming to create a large-scale but quality-unguaranteed corpus. In this work, we investigate Chinese OEI with extremely-noisy crowdsourcing annotations, constructing a dataset at a very low cost. Following zhang et al. (2021), we train the annotator-adapter model by regarding all annotations as gold-standard in terms of crowd annotators, and test the model by using a synthetic expert, which is a mixture of all annotators. As this annotator-mixture for testing is never modeled explicitly in the training phase, we propose to generate synthetic training samples by a pertinent mixup strategy to make the training and testing highly consistent. The simulation experiments on our constructed dataset show that crowdsourcing is highly promising for OEI, and our proposed annotator-mixup can further enhance the crowdsourcing modeling.
Persona-Guided Planning for Controlling the Protagonist's Persona in Story Generation
Authors: Zhexin Zhang, Jiaxin Wen, Jian Guan, Minlie Huang
Date: 2022-04-22 00:00:00
Tasks: Sentence, Story Generation

Endowing the protagonist with a specific personality is essential for writing an engaging story. In this paper, we aim to control the protagonist's persona in story generation, i.e., generating a story from a leading context and a persona description, where the protagonist should exhibit the specified personality through a coherent event sequence. Considering that personas are usually embodied implicitly and sparsely in stories, we propose a planning-based generation model named CONPER to explicitly model the relationship between personas and events. CONPER first plans events of the protagonist's behavior which are motivated by the specified persona through predicting one target sentence, then plans the plot as a sequence of keywords with the guidance of the predicted persona-related events and commonsense knowledge, and finally generates the whole story. Both automatic and manual evaluation results demonstrate that CONPER outperforms state-of-the-art baselines for generating more coherent and persona-controllable stories.
Autoregressive Search Engines: Generating Substrings as Document Identifiers
Authors: Michele Bevilacqua, Giuseppe Ottaviano, Patrick Lewis, Wen-tau Yih, Sebastian Riedel, Fabio Petroni
Date: 2022-04-22 00:00:00
Tasks: Information Retrieval, Retrieval

Knowledge-intensive language tasks require NLP systems to both provide the correct answer and retrieve supporting evidence for it in a given corpus. Autoregressive language models are emerging as the de-facto standard for generating answers, with newer and more powerful systems emerging at an astonishing pace. In this paper we argue that all this (and future) progress can be directly applied to the retrieval problem with minimal intervention to the models' architecture. Previous work has explored ways to partition the search space into hierarchical structures and retrieve documents by autoregressively generating their unique identifier. In this work we propose an alternative that doesn't force any structure in the search space: using all ngrams in a passage as its possible identifiers. This setup allows us to use an autoregressive model to generate and score distinctive ngrams, that are then mapped to full passages through an efficient data structure. Empirically, we show this not only outperforms prior autoregressive approaches but also leads to an average improvement of at least 10 points over more established retrieval solutions for passage-level retrieval on the KILT benchmark, establishing new state-of-the-art downstream performance on some datasets, while using a considerably lighter memory footprint than competing systems. Code and pre-trained models at https://github.com/facebookresearch/SEAL.
Learning Functional Distributional Semantics with Visual Data
Authors: Yinhong Liu, Guy Emerson
Date: 2022-04-22 00:00:00
Proceeding: ACL 2022 5
Tasks: Language Acquisition

Functional Distributional Semantics is a recently proposed framework for learning distributional semantics that provides linguistic interpretability. It models the meaning of a word as a binary classifier rather than a numerical vector. In this work, we propose a method to train a Functional Distributional Semantics model with grounded visual data. We train it on the Visual Genome dataset, which is closer to the kind of data encountered in human language acquisition than a large text corpus. On four external evaluation datasets, our model outperforms previous work on learning semantics from Visual Genome.
Out-of-Domain Evaluation of Finnish Dependency Parsing
Authors: Jenna Kanerva, Filip Ginter
Date: 2022-04-22 00:00:00
Proceeding: LREC 2022 6
Tasks: Dependency Parsing

The prevailing practice in the academia is to evaluate the model performance on in-domain evaluation data typically set aside from the training corpus. However, in many real world applications the data on which the model is applied may very substantially differ from the characteristics of the training data. In this paper, we focus on Finnish out-of-domain parsing by introducing a novel UD Finnish-OOD out-of-domain treebank including five very distinct data sources (web documents, clinical, online discussions, tweets, and poetry), and a total of 19,382 syntactic words in 2,122 sentences released under the Universal Dependencies framework. Together with the new treebank, we present extensive out-of-domain parsing evaluation utilizing the available section-level information from three different Finnish UD treebanks (TDT, PUD, OOD). Compared to the previously existing treebanks, the new Finnish-OOD is shown include sections more challenging for the general parser, creating an interesting evaluation setting and yielding valuable information for those applying the parser outside of its training domain.
Generalized Quantifiers as a Source of Error in Multilingual NLU Benchmarks
Authors: Ruixiang Cui, Daniel Hershcovich, Anders Søgaard
Date: 2022-04-22 00:00:00
Proceeding: NAACL (DADC) 2022 7

Logical approaches to representing language have developed and evaluated computational models of quantifier words since the 19th century, but today's NLU models still struggle to capture their semantics. We rely on Generalized Quantifier Theory for language-independent representations of the semantics of quantifier words, to quantify their contribution to the errors of NLU models. We find that quantifiers are pervasive in NLU benchmarks, and their occurrence at test time is associated with performance drops. Multilingual models also exhibit unsatisfying quantifier reasoning abilities, but not necessarily worse for non-English languages. To facilitate directly-targeted probing, we present an adversarial generalized quantifier NLI task (GQNLI) and show that pre-trained language models have a clear lack of robustness in generalized quantifier reasoning.
LibriS2S: A German-English Speech-to-Speech Translation Corpus
Authors: Pedro Jeuris, Jan Niehues
Date: 2022-04-22 00:00:00
Proceeding: LREC 2022 6
Tasks: Speech-to-Speech Translation, Speech-to-Text, Speech-to-Text Translation, text-to-speech, Text to Speech, Translation

Recently, we have seen an increasing interest in the area of speech-to-text translation. This has led to astonishing improvements in this area. In contrast, the activities in the area of speech-to-speech translation is still limited, although it is essential to overcome the language barrier. We believe that one of the limiting factors is the availability of appropriate training data. We address this issue by creating LibriS2S, to our knowledge the first publicly available speech-to-speech training corpus between German and English. For this corpus, we used independently created audio for German and English leading to an unbiased pronunciation of the text in both languages. This allows the creation of a new text-to-speech and speech-to-speech translation model that directly learns to generate the speech signal based on the pronunciation of the source language. Using this created corpus, we propose Text-to-Speech models based on the example of the recently proposed FastSpeech 2 model that integrates source language information. We do this by adapting the model to take information such as the pitch, energy or transcript from the source speech as additional input.
SalesBot: Transitioning from Chit-Chat to Task-Oriented Dialogues
Authors: Ssu Chiu, Maolin Li, Yen-Ting Lin, Yun-Nung Chen
Date: 2022-04-22 00:00:00
Proceeding: ACL 2022 5
Tasks: Dialogue Generation

Dialogue systems are usually categorized into two types, open-domain and task-oriented. The first one focuses on chatting with users and making them engage in the conversations, where selecting a proper topic to fit the dialogue context is essential for a successful dialogue. The other one focuses on a specific task instead of casual talks, e.g., finding a movie on Friday night, or playing a song. These two directions have been studied separately due to their different purposes. However, how smoothly transitioning from social chatting to task-oriented dialogues is important for triggering business opportunities, and there is no public data focusing on such scenarios. Hence, this paper focuses on investigating the conversations starting from open-domain social chatting and then gradually transitioning to task-oriented purposes, and releases a large-scale dataset with detailed annotations for encouraging this research direction. To achieve this goal, this paper proposes a framework to automatically generate many dialogues without human involvement, in which any powerful open-domain dialogue generation model can be easily leveraged. The human evaluation shows that our generated dialogue data has a natural flow at a reasonable quality, showing that our released data has a great potential of guiding future research directions and commercial activities. Furthermore, the released models allow researchers to automatically generate unlimited dialogues in the target scenarios, which can greatly benefit semi-supervised and unsupervised approaches.
Efficient Training of Neural Transducer for Speech Recognition
Authors: Wei Zhou, Wilfried Michel, Ralf Schlüter, Hermann Ney
Date: 2022-04-22 00:00:00
Tasks: GPU, speech-recognition, Speech Recognition

As one of the most popular sequence-to-sequence modeling approaches for speech recognition, the RNN-Transducer has achieved evolving performance with more and more sophisticated neural network models of growing size and increasing training epochs. While strong computation resources seem to be the prerequisite of training superior models, we try to overcome it by carefully designing a more efficient training pipeline. In this work, we propose an efficient 3-stage progressive training pipeline to build highly-performing neural transducer models from scratch with very limited computation resources in a reasonable short time period. The effectiveness of each stage is experimentally verified on both Librispeech and Switchboard corpora. The proposed pipeline is able to train transducer models approaching state-of-the-art performance with a single GPU in just 2-3 weeks. Our best conformer transducer achieves 4.1% WER on Librispeech test-other with only 35 epochs of training.
KALA: Knowledge-Augmented Language Model Adaptation
Authors: Minki Kang, Jinheon Baek, Sung Ju Hwang
Date: 2022-04-22 00:00:00
Proceeding: NAACL 2022 7
Tasks: Domain Adaptation, General Knowledge, Language Modeling, Language Modelling, model, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), Natural Language Understanding, Question Answering

Pre-trained language models (PLMs) have achieved remarkable success on various natural language understanding tasks. Simple fine-tuning of PLMs, on the other hand, might be suboptimal for domain-specific tasks because they cannot possibly cover knowledge from all domains. While adaptive pre-training of PLMs can help them obtain domain-specific knowledge, it requires a large training cost. Moreover, adaptive pre-training can harm the PLM's performance on the downstream task by causing catastrophic forgetting of its general knowledge. To overcome such limitations of adaptive pre-training for PLM adaption, we propose a novel domain adaption framework for PLMs coined as Knowledge-Augmented Language model Adaptation (KALA), which modulates the intermediate hidden representations of PLMs with domain knowledge, consisting of entities and their relational facts. We validate the performance of our KALA on question answering and named entity recognition tasks on multiple datasets across various domains. The results show that, despite being computationally efficient, our KALA largely outperforms adaptive pre-training. Code is available at: https://github.com/Nardien/KALA/.
Zero and Few-shot Learning for Author Profiling
Authors: Mara Chinea-Rios, Thomas Müller, Gretel Liz De la Peña Sarracén, Francisco Rangel, Marc Franco-Salvador
Date: 2022-04-22 00:00:00
Tasks: Author Profiling, Few-Shot Learning

Author profiling classifies author characteristics by analyzing how language is shared among people. In this work, we study that task from a low-resource viewpoint: using little or no training data. We explore different zero and few-shot models based on entailment and evaluate our systems on several profiling tasks in Spanish and English. In addition, we study the effect of both the entailment hypothesis and the size of the few-shot training sample. We find that entailment-based models out-perform supervised text classifiers based on roberta-XLM and that we can reach 80% of the accuracy of previous approaches using less than 50\% of the training data on average.
Rethinking Offensive Text Detection as a Multi-Hop Reasoning Problem
Authors: Qiang Zhang, Jason Naradowsky, Yusuke Miyao
Date: 2022-04-22 00:00:00
Proceeding: Findings (ACL) 2022 5
Tasks: Text Detection

We introduce the task of implicit offensive text detection in dialogues, where a statement may have either an offensive or non-offensive interpretation, depending on the listener and context. We argue that reasoning is crucial for understanding this broader class of offensive utterances and release SLIGHT, a dataset to support research on this task. Experiments using the data show that state-of-the-art methods of offense detection perform poorly when asked to detect implicitly offensive statements, achieving only ${\sim} 11\%$ accuracy. In contrast to existing offensive text detection datasets, SLIGHT features human-annotated chains of reasoning which describe the mental process by which an offensive interpretation can be reached from each ambiguous statement. We explore the potential for a multi-hop reasoning approach by utilizing existing entailment models to score the probability of these chains and show that even naive reasoning models can yield improved performance in most situations. Furthermore, analysis of the chains provides insight into the human interpretation process and emphasizes the importance of incorporating additional commonsense knowledge.
Towards Multi-Turn Empathetic Dialogs with Positive Emotion Elicitation
Authors: Shihang Wang, Xinchao Xu, Wenquan Wu, Zheng-Yu Niu, Hua Wu, Haifeng Wang
Date: 2022-04-22 00:00:00

Emotional support is a crucial skill for many real-world scenarios, including caring for the elderly, mental health support, and customer service chats. This paper presents a novel task of empathetic dialog generation with positive emotion elicitation to promote users' positive emotions, similar to that of emotional support between humans. In this task, the agent conducts empathetic responses along with the target of eliciting the user's positive emotions in the multi-turn dialog. To facilitate the study of this task, we collect a large-scale emotional dialog dataset with positive emotion elicitation, called PosEmoDial (about 820k dialogs, 3M utterances). In these dialogs, the agent tries to guide the user from any possible initial emotional state, e.g., sadness, to a positive emotional state. Then we present a positive-emotion-guided dialog generation model with a novel loss function design. This loss function encourages the dialog model to not only elicit positive emotions from users but also ensure smooth emotional transitions along with the whole dialog. Finally, we establish benchmark results on PosEmoDial, and we will release this dataset and related source code to facilitate future studies.
WaBERT: A Low-resource End-to-end Model for Spoken Language Understanding and Speech-to-BERT Alignment
Authors: Lin Yao, Jianfei Song, Ruizhuo Xu, Yingfang Yang, Zijian Chen, Yafeng Deng
Date: 2022-04-22 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Language Modeling, Language Modelling, Sentiment Analysis, Speaker Identification, speech-recognition, Speech Recognition, Speech-to-Text, Spoken Language Understanding

Historically lower-level tasks such as automatic speech recognition (ASR) and speaker identification are the main focus in the speech field. Interest has been growing in higher-level spoken language understanding (SLU) tasks recently, like sentiment analysis (SA). However, improving performances on SLU tasks remains a big challenge. Basically, there are two main methods for SLU tasks: (1) Two-stage method, which uses a speech model to transfer speech to text, then uses a language model to get the results of downstream tasks; (2) One-stage method, which just fine-tunes a pre-trained speech model to fit in the downstream tasks. The first method loses emotional cues such as intonation, and causes recognition errors during ASR process, and the second one lacks necessary language knowledge. In this paper, we propose the Wave BERT (WaBERT), a novel end-to-end model combining the speech model and the language model for SLU tasks. WaBERT is based on the pre-trained speech and language model, hence training from scratch is not needed. We also set most parameters of WaBERT frozen during training. By introducing WaBERT, audio-specific information and language knowledge are integrated in the short-time and low-resource training process to improve results on the dev dataset of SLUE SA tasks by 1.15% of recall score and 0.82% of F1 score. Additionally, we modify the serial Continuous Integrate-and-Fire (CIF) mechanism to achieve the monotonic alignment between the speech and text modalities.
Improving the Generalizability of Depression Detection by Leveraging Clinical Questionnaires
Authors: Thong Nguyen, Andrew Yates, Ayah Zirikly, Bart Desmet, Arman Cohan
Date: 2022-04-21 00:00:00
Proceeding: ACL 2022 5
Tasks: Depression Detection, Domain Generalization

Automated methods have been widely used to identify and analyze mental health conditions (e.g., depression) from various sources of information, including social media. Yet, deployment of such models in real-world healthcare applications faces challenges including poor out-of-domain generalization and lack of trust in black box models. In this work, we propose approaches for depression detection that are constrained to different degrees by the presence of symptoms described in PHQ9, a questionnaire used by clinicians in the depression screening process. In dataset-transfer experiments on three social media datasets, we find that grounding the model in PHQ9's symptoms substantially improves its ability to generalize to out-of-distribution data compared to a standard BERT-based approach. Furthermore, this approach can still perform competitively on in-domain data. These results and our qualitative analyses suggest that grounding model predictions in clinically-relevant symptoms can improve generalizability while producing a model that is easier to inspect.
German Parliamentary Corpus (GerParCor)
Authors: Giuseppe Abrami, Mevlüt Bagci, Leon Hammerla, Alexander Mehler
Date: 2022-04-21 00:00:00
Proceeding: LREC 2022 6
Tasks: Optical Character Recognition (OCR)

Parliamentary debates represent a large and partly unexploited treasure trove of publicly accessible texts. In the German-speaking area, there is a certain deficit of uniformly accessible and annotated corpora covering all German-speaking parliaments at the national and federal level. To address this gap, we introduce the German Parliament Corpus (GerParCor). GerParCor is a genre-specific corpus of (predominantly historical) German-language parliamentary protocols from three centuries and four countries, including state and federal level data. In addition, GerParCor contains conversions of scanned protocols and, in particular, of protocols in Fraktur converted via an OCR process based on Tesseract. All protocols were preprocessed by means of the NLP pipeline of spaCy3 and automatically annotated with metadata regarding their session date. GerParCor is made available in the XMI format of the UIMA project. In this way, GerParCor can be used as a large corpus of historical texts in the field of political communication for various tasks in NLP.
Towards an Enhanced Understanding of Bias in Pre-trained Neural Language Models: A Survey with Special Emphasis on Affective Bias
Authors: Anoop K., Manjary P. Gangan, Deepak P., Lajish V. L
Date: 2022-04-21 00:00:00

The remarkable progress in Natural Language Processing (NLP) brought about by deep learning, particularly with the recent advent of large pre-trained neural language models, is brought into scrutiny as several studies began to discuss and report potential biases in NLP applications. Bias in NLP is found to originate from latent historical biases encoded by humans into textual data which gets perpetuated or even amplified by NLP algorithm. We present a survey to comprehend bias in large pre-trained language models, analyze the stages at which they occur in these models, and various ways in which these biases could be quantified and mitigated. Considering wide applicability of textual affective computing based downstream tasks in real-world systems such as business, healthcare, education, etc., we give a special emphasis on investigating bias in the context of affect (emotion) i.e., Affective Bias, in large pre-trained language models. We present a summary of various bias evaluation corpora that help to aid future research and discuss challenges in the research on bias in pre-trained language models. We believe that our attempt to draw a comprehensive view of bias in pre-trained language models, and especially the exploration of affective bias will be highly beneficial to researchers interested in this evolving field.