pwc-archive/papers-with-abstracts huggingface dataset:
Dr. Livingstone, I presume? Polishing of foreign character identification in literary texts
Authors: Aleksandra Konovalova, Antonio Toral, Kristiina Taivalkoski-Shilov
Date: 2022-07-01 00:00:00
Proceeding: NAACL (ACL) 2022 7
Tasks: LEMMA, Translation

Character identification is a key element for many narrative-related tasks. To implement it, the baseform of the name of the character (or lemma) needs to be identified, so different appearances of the same character in the narrative could be aligned. In this paper we tackle this problem in translated texts (English–Finnish translation direction), where the challenge regarding lemmatizing foreign names in an agglutinative language appears. To solve this problem, we present and compare several methods. The results show that the method based on a search for the shortest version of the name proves to be the easiest, best performing (83.4% F1), and most resource-independent.
Zuo Zhuan Ancient Chinese Dataset for Word Sense Disambiguation
Authors: Xiaomeng Pan, Hongfei Wang, Teruaki Oka, Mamoru Komachi
Date: 2022-07-01 00:00:00
Proceeding: NAACL (ACL) 2022 7
Tasks: Language Modeling, Language Modelling, Word Sense Disambiguation

Word Sense Disambiguation (WSD) is a core task in Natural Language Processing (NLP). Ancient Chinese has rarely been used in WSD tasks, however, as no public dataset for ancient Chinese WSD tasks exists. Creation of an ancient Chinese dataset is considered a significant challenge because determining the most appropriate sense in a context is difficult and time-consuming owing to the different usages in ancient and modern Chinese. Actually, no public dataset for ancient Chinese WSD tasks exists. To solve the problem of ancient Chinese WSD, we annotate part of Pre-Qin (221 BC) text Zuo Zhuan using a copyright-free dictionary to create a public sense-tagged dataset. Then, we apply a simple Nearest Neighbors (k-NN) method using a pre-trained language model to the dataset. Our code and dataset will be available on GitHub.
How do people talk about images? A study on open-domain conversations with images.
Authors: Yi-Pei Chen, Nobuyuki Shimizu, Takashi Miyazaki, Hideki Nakayama
Date: 2022-07-01 00:00:00
Proceeding: NAACL (ACL) 2022 7
Tasks: Diversity

This paper explores how humans conduct conversations with images by investigating an open-domain image conversation dataset, ImageChat. We examined the conversations with images from the perspectives of \textit{image relevancy} and \textit{image information}. We found that utterances/conversations are not always related to the given image, and conversation topics diverge within three turns about half of the time. Besides image objects, more comprehensive non-object image information is also indispensable. After inspecting the causes, we suggested that understanding the overall scenario of image and connecting objects based on their high-level attributes might be very helpful to generate more engaging open-domain conversations when an image is presented. We proposed enriching the image information with image caption and object tags based on our analysis. With our proposed \textit{image}^{+} features, we improved automatic metrics including BLEU and Bert Score, and increased the diversity and image-relevancy of generated responses to the strong baseline. The result verifies that our analysis provides valuable insights and could facilitate future research on open-domain conversations with images.
Differentially Private Instance Encoding against Privacy Attacks
Authors: Shangyu Xie, Yuan Hong
Date: 2022-07-01 00:00:00
Proceeding: NAACL (ACL) 2022 7
Tasks: Reconstruction Attack

TextHide was recently proposed to protect the training data via instance encoding in natural language domain. Due to the lack of theoretic privacy guarantee, such instance encoding scheme has been shown to be vulnerable against privacy attacks, e.g., reconstruction attack. To address such limitation, we revise the instance encoding scheme with differential privacy and thus provide a provable guarantee against privacy attacks. The experimental results also show that the proposed scheme can defend against privacy attacks while ensuring learning utility (as a trade-off).
Multimodal Modeling of Task-Mediated Confusion
Authors: Camille Mince, Skye Rhomberg, Cecilia Alm, Reynold Bailey, Alex Ororbia
Date: 2022-07-01 00:00:00
Proceeding: NAACL (ACL) 2022 7

In order to build more human-like cognitive agents, systems capable of detecting various human emotions must be designed to respond appropriately. Confusion, the combination of an emotional and cognitive state, is under-explored. In this paper, we build upon prior work to develop models that detect confusion from three modalities: video (facial features), audio (prosodic features), and text (transcribed speech features). Our research improves the data collection process by allowing for continuous (as opposed to discrete) annotation of confusion levels. We also craft models based on recurrent neural networks (RNNs) given their ability to predict sequential data. In our experiments, we find that text and video modalities are the most important in predicting confusion while the explored audio features are relatively unimportant predictors of confusion in our data.
Probe-Less Probing of BERT’s Layer-Wise Linguistic Knowledge with Masked Word Prediction
Authors: Tatsuya Aoyama, Nathan Schneider
Date: 2022-07-01 00:00:00
Proceeding: NAACL (ACL) 2022 7
Tasks: Prediction

The current study quantitatively (and qualitatively for an illustrative purpose) analyzes BERT’s layer-wise masked word prediction on an English corpus, and finds that (1) the layerwise localization of linguistic knowledge primarily shown in probing studies is replicated in a behavior-based design and (2) that syntactic and semantic information is encoded at different layers for words of different syntactic categories. Hypothesizing that the above results are correlated with the number of likely potential candidates of the masked word prediction, we also investigate how the results differ for tokens within multiword expressions.
Multimodal large language models for inclusive collaboration learning tasks
Authors: Armanda Lewis
Date: 2022-07-01 00:00:00
Proceeding: NAACL (ACL) 2022 7
Tasks: Learning Theory

This PhD project leverages advancements in multimodal large language models to build an inclusive collaboration feedback loop, in order to facilitate the automated detection, modeling, and feedback for participants developing general collaboration skills. This topic is important given the role of collaboration as an essential 21st century skill, the potential to ground large language models within learning theory and real-world practice, and the expressive potential of transformer models to support equity and inclusion. We address some concerns of integrating advances in natural language processing into downstream tasks such as the learning analytics feedback loop.
Neural Networks in a Product of Hyperbolic Spaces
Authors: Jun Takeuchi, Noriki Nishida, Hideki Nakayama
Date: 2022-07-01 00:00:00
Proceeding: NAACL (ACL) 2022 7
Tasks: Graph Embedding, Knowledge Graph Embedding, Machine Translation, Node Classification

Machine learning in hyperbolic spaces has attracted much attention in natural language processing and many other fields. In particular, Hyperbolic Neural Networks (HNNs) have improved a wide variety of tasks, from machine translation to knowledge graph embedding. Although some studies have reported the effectiveness of embedding into the product of multiple hyperbolic spaces, HNNs have mainly been constructed in a single hyperbolic space, and their extension to product spaces has not been sufficiently studied. Therefore, we propose a novel method to extend a given HNN in a single space to a product of hyperbolic spaces. We apply our method to Hyperbolic Graph Convolutional Networks (HGCNs), extending several HNNs. Our model improved the graph node classification accuracy especially on datasets with tree-like structures. The results suggest that neural networks in a product of hyperbolic spaces can be more effective than in a single space in representing structural data.
Explicit Use of Topicality in Dialogue Response Generation
Authors: Takumi Yoshikoshi, Hayato Atarashi, Takashi Kodama, Sadao Kurohashi
Date: 2022-07-01 00:00:00
Proceeding: NAACL (ACL) 2022 7
Tasks: Response Generation, Self-Supervised Learning

The current chat dialogue systems implicitly consider the topic given the context, but not explicitly. As a result, these systems often generate inconsistent responses with the topic of the moment. In this study, we propose a dialogue system that responds appropriately following the topic by selecting the entity with the highest “topicality.” In topicality estimation, the model is trained through self-supervised learning that regards entities that appear in both context and response as the topic entities. In response generation, the model is trained to generate topic-relevant responses based on the estimated topicality. Experimental results show that our proposed system can follow the topic more than the existing dialogue system that considers only the context.
Automating Human Evaluation of Dialogue Systems
Authors: Sujan Reddy A
Date: 2022-07-01 00:00:00
Proceeding: NAACL (ACL) 2022 7
Tasks: Informativeness

Automated metrics to evaluate dialogue systems like BLEU, METEOR, etc., weakly correlate with human judgments. Thus, human evaluation is often used to supplement these metrics for system evaluation. However, human evaluation is time-consuming as well as expensive. This paper provides an alternative approach to human evaluation with respect to three aspects: naturalness, informativeness, and quality in dialogue systems. I propose an approach based on fine-tuning the BERT model with three prediction heads, to predict whether the system-generated output is natural, fluent, and informative. I observe that the proposed model achieves an average accuracy of around 77% over these 3 labels. I also design a baseline approach that uses three different BERT models to make the predictions. Based on experimental analysis, I find that using a shared model to compute the three labels performs better than three separate models.
Static and Dynamic Speaker Modeling based on Graph Neural Network for Emotion Recognition in Conversation
Authors: Prakhar Saxena, Yin Jou Huang, Sadao Kurohashi
Date: 2022-07-01 00:00:00
Proceeding: NAACL (ACL) 2022 7
Tasks: Emotion Recognition, Emotion Recognition in Conversation, Graph Neural Network

Each person has a unique personality which affects how they feel and convey emotions. Hence, speaker modeling is important for the task of emotion recognition in conversation (ERC). In this paper, we propose a novel graph-based ERC model which considers both conversational context and speaker personality. We model the internal state of the speaker (personality) as Static and Dynamic speaker state, where the Dynamic speaker state is modeled with a graph neural network based encoder. Experiments on benchmark dataset shows the effectiveness of our model. Our model outperforms baseline and other graph-based methods. Analysis of results also show the importance of explicit speaker modeling.
Few-shot fine-tuning SOTA summarization models for medical dialogues
Authors: David Fraile Navarro, Mark Dras, Shlomo Berkovsky
Date: 2022-07-01 00:00:00
Proceeding: NAACL (ACL) 2022 7
Tasks: Abstractive Text Summarization, Few-Shot Learning

Abstractive summarization of medical dialogues presents a challenge for standard training approaches, given the paucity of suitable datasets. We explore the performance of state-of-the-art models with zero-shot and few-shot learning strategies and measure the impact of pretraining with general domain and dialogue-specific text on the summarization performance.
Unifying Parsing and Tree-Structured Models for Generating Sentence Semantic Representations
Authors: Antoine Simoulin, Benoit Crabbé
Date: 2022-07-01 00:00:00
Proceeding: NAACL (ACL) 2022 7
Tasks: Sentence, Sentence Embedding, Sentence-Embedding, Sentence Embeddings

We introduce a novel tree-based model that learns its composition function together with its structure. The architecture produces sentence embeddings by composing words according to an induced syntactic tree. The parsing and the composition functions are explicitly connected and, therefore, learned jointly. As a result, the sentence embedding is computed according to an interpretable linguistic pattern and may be used on any downstream task. We evaluate our encoder on downstream tasks, and we observe that it outperforms tree-based models relying on external parsers. In some configurations, it is even competitive with Bert base model. Our model is capable of supporting multiple parser architectures. We exploit this property to conduct an ablation study by comparing different parser initializations. We explore to which extent the trees produced by our model compare with linguistic structures and how this initialization impacts downstream performances. We empirically observe that downstream supervision troubles producing stable parses and preserving linguistically relevant structures.
Exploring the Effect of Dialect Mismatched Language Models in Telugu Automatic Speech Recognition
Authors: Aditya Yadavalli, Ganesh Sai Mirishkar, Anil Vuppala
Date: 2022-07-01 00:00:00
Proceeding: NAACL (ACL) 2022 7
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Language Modeling, Language Modelling, speech-recognition, Speech Recognition

Previous research has found that Acoustic Models (AM) of an Automatic Speech Recognition (ASR) system are susceptible to dialect variations within a language, thereby adversely affecting the ASR. To counter this, researchers have proposed to build a dialect-specific AM while keeping the Language Model (LM) constant for all the dialects. This study explores the effect of dialect mismatched LM by considering three different Telugu regional dialects: Telangana, Coastal Andhra, and Rayalaseema. We show that dialect variations that surface in the form of a different lexicon, grammar, and occasionally semantics can significantly degrade the performance of the LM under mismatched conditions. Therefore, this degradation has an adverse effect on the ASR even when dialect-specific AM is used. We show a degradation of up to 13.13 perplexity points when LM is used under mismatched conditions. Furthermore, we show a degradation of over 9% and over 15% in Character Error Rate (CER) and Word Error Rate (WER), respectively, in the ASR systems when using mismatched LMs over matched LMs.
Web-based Annotation Interface for Derivational Morphology
Authors: Lukáš Kyjánek
Date: 2022-07-01 00:00:00
Proceeding: NAACL (ACL) 2022 7

The paper presents a visual interface for manual annotation of language resources for derivational morphology. The interface is web-based and created using relatively simple programming techniques, and yet it rapidly facilitates and speeds up the annotation process, especially in languages with rich derivational morphology. As such, it can reduce the cost of the process. After introducing manual annotation tasks in derivational morphology, the paper describes the new visual interface and a case study that compares the current annotation method to the annotation using the interface. In addition, it also demonstrates the opportunity to use the interface for manual annotation of syntactic trees. The source codes are freely available under the MIT License on GitHub.
TurkishDelightNLP: A Neural Turkish NLP Toolkit
Authors: Huseyin Alecakir, Necva Bölücü, Burcu Can
Date: 2022-07-01 00:00:00
Proceeding: NAACL (ACL) 2022 7
Tasks: Dependency Parsing, Morphological Tagging, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), Part-Of-Speech Tagging, Semantic Parsing, Word Embeddings, Word Similarity

We introduce a neural Turkish NLP toolkit called TurkishDelightNLP that performs computational linguistic analyses from morphological level to semantic level that involves tasks such as stemming, morphological segmentation, morphological tagging, part-of-speech tagging, dependency parsing, and semantic parsing, as well as high-level NLP tasks such as named entity recognition. We publicly share the open-source Turkish NLP toolkit through a web interface that allows an input text to be analysed in real-time, as well as the open source implementation of the components provided in the toolkit, an API, and several annotated datasets such as word similarity test set to evaluate word embeddings and UCCA-based semantic annotation in Turkish. This will be the first open-source Turkish NLP toolkit that involves a range of NLP tasks in all levels. We believe that it will be useful for other researchers in Turkish NLP and will be also beneficial for other high-level NLP tasks in Turkish.
Contrastive Explanations of Text Classifiers as a Service
Authors: Lorenzo Malandri, Fabio Mercorio, Mario Mezzanzanica, Navid Nobani, Andrea Seveso
Date: 2022-07-01 00:00:00
Proceeding: NAACL (ACL) 2022 7

The recent growth of black-box machine-learning methods in data analysis has increased the demand for explanation methods and tools to understand their behaviour and assist human-ML model cooperation. In this paper, we demonstrate ContrXT, a novel approach that uses natural language explanations to help users to comprehend how a back-box model works. ContrXT provides time contrastive (t-contrast) explanations by computing the differences in the classification logic of two different trained models and then reasoning on their symbolic representations through Binary Decision Diagrams. ContrXT is publicly available at ContrXT.ai as a python pip package.
RESIN-11: Schema-guided Event Prediction for 11 Newsworthy Scenarios
Authors: Xinya Du, Zixuan Zhang, Sha Li, Pengfei Yu, Hongwei Wang, Tuan Lai, Xudong Lin, Ziqi Wang, Iris Liu, Ben Zhou, Haoyang Wen, Manling Li, Darryl Hannan, Jie Lei, Hyounghun Kim, Rotem Dror, Haoyu Wang, Michael Regan, Qi Zeng, Qing Lyu, Charles Yu, Carl Edwards, Xiaomeng Jin, Yizhu Jiao, Ghazaleh Kazeminejad, Zhenhailong Wang, Chris Callison-Burch, Mohit Bansal, Carl Vondrick, Jiawei Han, Dan Roth, Shih-Fu Chang, Martha Palmer, Heng Ji
Date: 2022-07-01 00:00:00
Proceeding: NAACL (ACL) 2022 7
Tasks: Event Extraction, Prediction

We introduce RESIN-11, a new schema-guided event extraction&prediction framework that can be applied to a large variety of newsworthy scenarios. The framework consists of two parts: (1) an open-domain end-to-end multimedia multilingual information extraction system with weak-supervision and zero-shot learningbased techniques. (2) schema matching and schema-guided event prediction based on our curated schema library. We build a demo website based on our dockerized system and schema library publicly available for installation (https://github.com/RESIN-KAIROS/RESIN-11). We also include a video demonstrating the system.
A Human-machine Interface for Few-shot Rule Synthesis for Information Extraction
Authors: Robert Vacareanu, George C.G. Barbosa, Enrique Noriega-Atala, Gus Hahn-Powell, Rebecca Sharp, Marco A. Valenzuela-Escárcega, Mihai Surdeanu
Date: 2022-07-01 00:00:00
Proceeding: NAACL (ACL) 2022 7
Tasks: Relation Extraction

We propose a system that assists a user in constructing transparent information extraction models, consisting of patterns (or rules) written in a declarative language, through program synthesis.Users of our system can specify their requirements through the use of examples,which are collected with a search interface.The rule-synthesis system proposes rule candidates and the results of applying them on a textual corpus; the user has the option to accept the candidate, request another option, or adjust the examples provided to the system.Through an interactive evaluation, we show that our approach generates high-precision rules even in a 1-shot setting. On a second evaluation on a widely-used relation extraction dataset (TACRED), our method generates rules that outperform considerably manually written patterns.Our code, demo, and documentation is available at https://clulab.github.io/odinsynth.
SentSpace: Large-Scale Benchmarking and Evaluation of Text using Cognitively Motivated Lexical, Syntactic, and Semantic Features
Authors: Greta Tuckute, Aalok Sathe, Mingye Wang, Harley Yoder, Cory Shain, Evelina Fedorenko
Date: 2022-07-01 00:00:00
Proceeding: NAACL (ACL) 2022 7
Tasks: Benchmarking, Sentence

SentSpace is a modular framework for streamlined evaluation of text. SentSpacecharacterizes textual input using diverse lexical, syntactic, and semantic features derivedfrom corpora and psycholinguistic experiments. Core sentence features fall into three primaryfeature spaces: 1) Lexical, 2) Contextual, and 3) Embeddings. To aid in the analysis of computed features, SentSpace provides a web interface for interactive visualization and comparison with text from large corpora. The modular design of SentSpace allows researchersto easily integrate their own feature computation into the pipeline while benefiting from acommon framework for evaluation and visualization. In this manuscript we will describe thedesign of SentSpace, its core feature spaces, and demonstrate an example use case by comparing human-written and machine-generated (GPT2-XL) sentences to each other. We findthat while GPT2-XL-generated text appears fluent at the surface level, psycholinguistic normsand measures of syntactic processing reveal key differences between text produced by humansand machines. Thus, SentSpace provides a broad set of cognitively motivated linguisticfeatures for evaluation of text within natural language processing, cognitive science, as wellas the social sciences.
DadmaTools: Natural Language Processing Toolkit for Persian Language
Authors: Romina Etezadi, Mohammad Karrabi, Najmeh Zare, Mohamad Bagher Sajadi, Mohammad Taher Pilehvar
Date: 2022-07-01 00:00:00
Proceeding: NAACL (ACL) 2022 7
Tasks: Chunking, Constituency Parsing, Dependency Parsing, Lemmatization

We introduce DadmaTools, an open-source Python Natural Language Processing toolkit for the Persian language. The toolkit is a neural pipeline based on spaCy for several text processing tasks, including normalization, tokenization, lemmatization, part-of-speech, dependency parsing, constituency parsing, chunking, and ezafe detecting. DadmaTools relies on fine-tuning of ParsBERT using the PerDT dataset for most of the tasks. Dataset module and embedding module are included in DadmaTools that support different Persian datasets, embeddings, and commonly used functions for them. Our evaluations show that DadmaTools can attain state-of-the-art performance on multiple NLP tasks. The source code is freely available at https://github.com/Dadmatech/DadmaTools.