pwc-archive/papers-with-abstracts huggingface dataset:
PerKGQA: Question Answering over Personalized Knowledge Graphs
Authors: Ritam Dutt, Kasturi Bhattacharjee, Rashmi Gangadharaiah, Dan Roth, Carolyn Rose
Date: 2022-07-01 00:00:00
Proceeding: Findings (NAACL) 2022 7
Tasks: Knowledge Graphs, Question Answering

Previous studies on question answering over knowledge graphs have typically operated over a single knowledge graph (KG). This KG is assumed to be known a priori and is lever- aged similarly for all users’ queries during inference. However, such an assumption is not applicable to real-world settings, such as health- care, where one needs to handle queries of new users over unseen KGs during inference. Furthermore, privacy concerns and high computational costs render it infeasible to query the single KG that has information about all users while answering a specific user’s query. The above concerns motivate our question answer- ing setting over personalized knowledge graphs (PERKGQA) where each user has restricted access to their KG. We observe that current state-of-the-art KGQA methods that require learning prior node representations fare poorly. We propose two complementary approaches, PATHCBR and PATHRGCN for PERKGQA. The former is a simple non-parametric technique that employs case-based reasoning, while the latter is a parametric approach using graph neural networks. Our proposed methods circumvent learning prior representations, can generalize to unseen KGs, and outperform strong baselines on an academic and an internal dataset by 6.5% and 10.5%.
Exploring the Value of Multi-View Learning for Session-Aware Query Representation
Authors: Diego Ortiz, Jose Moreno, Gilles Hubert, Karen Pinel-Sauvagnat, Lynda Tamine
Date: 2022-07-01 00:00:00
Proceeding: Findings (NAACL) 2022 7
Tasks: Document Ranking, MULTI-VIEW LEARNING, Representation Learning, Retrieval

Recent years have witnessed a growing interest towards learning distributed query representations that are able to capture search intent semantics. Most existing approaches learn query embeddings using relevance supervision making them suited only to document ranking tasks. Besides, they generally consider either user’s query reformulations or system’s rankings whereas previous findings show that user’s query behavior and knowledge change depending on the system’s results, intertwine and affect each other during the completion of a search task. In this paper, we explore the value of multi-view learning for generic and unsupervised session-aware query representation learning. First, single-view query embeddings are obtained in separate spaces from query reformulations and document ranking representations using transformers. Then, we investigate the use of linear (CCA) and non linear (UMAP) multi-view learning methods, to align those spaces with the aim of revealing similarity traits in the multi-view shared space. Experimental evaluation is carried out in a query classification and session-based retrieval downstream tasks using respectively the KDD and TREC session datasets. The results show that multi-view learning is an effective and controllable approach for unsupervised learning of generic query representations and can reflect search behavior patterns.
PCEE-BERT: Accelerating BERT Inference via Patient and Confident Early Exiting
Authors: Zhen Zhang, Wei Zhu, Jinfan Zhang, Peng Wang, Rize Jin, Tae-Sun Chung
Date: 2022-07-01 00:00:00
Proceeding: Findings (NAACL) 2022 7
Tasks: Model Compression

BERT and other pretrained language models (PLMs) are ubiquitous in modern NLP. Even though PLMs are the state-of-the-art (SOTA) models for almost every NLP task (CITATION), the significant latency during inference prohibits wider industrial usage. In this work, we propose Patient and Confident Early Exiting BERT (PCEE-BERT), an off-the-shelf sample-dependent early exiting method that can work with different PLMs and can also work along with popular model compression methods. With a multi-exit BERT as the backbone model, PCEE-BERT will make the early exiting decision if enough numbers (patience parameter) of consecutive intermediate layers are confident about their predictions. The entropy value measures the confidence level of an intermediate layer’s prediction. Experiments on the GLUE benchmark demonstrate that our method outperforms previous SOTA early exiting methods. Ablation studies show that: (a) our method performs consistently well on other PLMs, such as ALBERT and TinyBERT; (b) PCEE-BERT can achieve different speed-up ratios by adjusting the patience parameter and the confidence threshold. The code for PCEE-BERT can be found at https://github.com/michael-wzhu/PCEE-BERT.
TEAM: A multitask learning based Taxonomy Expansion approach for Attach and Merge
Authors: Bornali Phukon, Anasua Mitra, Ranbir Sanasam, Priyankoo Sarmah
Date: 2022-07-01 00:00:00
Proceeding: Findings (NAACL) 2022 7
Tasks: Taxonomy Expansion

Taxonomy expansion is a crucial task. Most of Automatic expansion of taxonomy are of two types, attach and merge. In a taxonomy like WordNet, both merge and attach are integral parts of the expansion operations but majority of study consider them separately. This paper proposes a novel mult-task learning-based deep learning method known as Taxonomy Expansion with Attach and Merge (TEAM) that performs both the merge and attach operations. To the best of our knowledge this is the first study which integrates both merge and attach operations in a single model.The proposed models have been evaluated on three separate WordNet taxonomies, viz., Assamese, Bangla, and Hindi. From the various experimental setups, it is shown that TEAM outperforms its state-of-the-art counterparts for attach operation, and also provides highly encouraging performance for the merge operation.
Improving Few-Shot Relation Classification by Prototypical Representation Learning with Definition Text
Authors: Li Zhenzhen, Yuyang Zhang, Jian-Yun Nie, Dongsheng Li
Date: 2022-07-01 00:00:00
Proceeding: Findings (NAACL) 2022 7
Tasks: Few-Shot Relation Classification, Relation, Relation Classification, Representation Learning

Few-shot relation classification is difficult because the few instances available may not represent well the relation patterns. Some existing approaches explored extra information such as relation definition, in addition to the instances, to learn a better relation representation. However, the encoding of the extra information has been performed independently from the labeled instances. In this paper, we propose to learn a prototype encoder from relation definition in a way that is useful for relation instance classification. To this end, we use a joint training approach to train both a prototype encoder from definition and an instance encoder. Extensive experiments on several datasets demonstrate the effectiveness and usefulness of our prototype encoder from definition text, enabling us to outperform state-of-the-art approaches.
Multimodal Intent Discovery from Livestream Videos
Authors: Adyasha Maharana, Quan Tran, Franck Dernoncourt, Seunghyun Yoon, Trung Bui, Walter Chang, Mohit Bansal
Date: 2022-07-01 00:00:00
Proceeding: Findings (NAACL) 2022 7
Tasks: Intent Discovery, Video Summarization, Video Understanding

Individuals, educational institutions, and businesses are prolific at generating instructional video content such as “how-to” and tutorial guides. While significant progress has been made in basic video understanding tasks, identifying procedural intent within these instructional videos is a challenging and important task that remains unexplored but essential to video summarization, search, and recommendations. This paper introduces the problem of instructional intent identification and extraction from software instructional livestreams. We construct and present a new multimodal dataset consisting of software instructional livestreams and containing manual annotations for both detailed and abstract procedural intent that enable training and evaluation of joint video and text understanding models. We then introduce a multimodal cascaded cross-attention model to efficiently combine the weaker and noisier video signal with the more discriminative text signal. Our experiments show that our proposed model brings significant gains compared to strong baselines, including large-scale pretrained multimodal models. Our analysis further identifies that the task benefits from spatial as well as motion features extracted from videos, and provides insight on how the video signal is preferentially used for intent discovery. We also show that current models struggle to comprehend the nature of abstract intents, revealing important gaps in multimodal understanding and paving the way for future work.
Harmless Transfer Learning for Item Embeddings
Authors: Chengyue Gong, Xiaocong Du, Dhruv Choudhary, Bhargav Bhushanam, Qiang Liu, Arun Kejariwal
Date: 2022-07-01 00:00:00
Proceeding: Findings (NAACL) 2022 7
Tasks: Recommendation Systems, Transfer Learning

Learning embedding layers (for classes, words, items, etc.) is a key component of lots of applications, ranging from natural language processing, recommendation systems to electronic health records, etc.However, the frequency of real-world items follows a long-tail distribution in these applications, causing naive training methods perform poorly on the rare items. A line of previous works address this problem by transferring the knowledge from the frequent items to rare items by introducing an auxiliary transfer loss. However, when defined improperly, the transfer loss may introduce harmful biases and deteriorate the performance.In this work, we propose a harmless transfer learning framework that limits the impact of the potential biases in both the definition and optimization of the transfer loss. On the definition side, we reduce the bias in transfer loss by focusing on the items to which information from high-frequency items can be efficiently transferred. On the optimization side, we leverage a lexicographic optimization framework to efficiently incorporate the information of the transfer loss without hurting the minimization of the main prediction loss function. Our method serves as a plug-in module and significantly boosts the performance on a variety of NLP and recommendation system tasks.
StATIK: Structure and Text for Inductive Knowledge Graph Completion
Authors: Elan Markowitz, Keshav Balasubramanian, Mehrnoosh Mirtaheri, Murali Annavaram, Aram Galstyan, Greg Ver Steeg
Date: 2022-07-01 00:00:00
Proceeding: Findings (NAACL) 2022 7
Tasks: Inductive knowledge graph completion, Knowledge Graph Completion, Knowledge Graphs

Knowledge graphs (KGs) often represent knowledge bases that are incomplete. Machine learning models can alleviate this by helping automate graph completion. Recently, there has been growing interest in completing knowledge bases that are dynamic, where previously unseen entities may be added to the KG with many missing links. In this paper, we present StATIK–Structure And Text for Inductive Knowledge Completion. StATIK uses Language Models to extract the semantic information from text descriptions, while using Message Passing Neural Networks to capture the structural information. StATIK achieves state of the art results on three challenging inductive baselines. We further analyze our hybrid model through detailed ablation studies.
Language Models for Code-switch Detection of te reo Māori and English in a Low-resource Setting
Authors: Jesin James, Vithya Yogarajan, Isabella Shields, Catherine Watson, Peter Keegan, Keoni Mahelona, Peter-Lucas Jones
Date: 2022-07-01 00:00:00
Proceeding: Findings (NAACL) 2022 7
Tasks: Transfer Learning, Word Embeddings

Te reo Māori, New Zealand’s only indigenous language, is code-switched with English. Māori speakers are atleast bilingual, and the use of Māori is increasing in New Zealand English. Unfortunately, due to the minimal availability of resources, including digital data, Māori is under-represented in technological advances. Cloud-based multilingual systems such as Google and Microsoft Azure support Māori language detection. However, we provide experimental evidence to show that the accuracy of such systems is low when detecting Māori. Hence, with the support of Māori community, we collect Māori and bilingual data to use natural language processing (NLP) to improve Māori language detection. We train bilingual sub-word embeddings and provide evidence to show that our bilingual embeddings improve overall accuracy compared to the publicly-available monolingual embeddings. This improvement has been verified for various NLP tasks using three bilingual databases containing formal transcripts and informal social media data. We also show that BiLSTM with pre-trained Māori-English sub-word embeddings outperforms large-scale contextual language models such as BERT on down streaming tasks of detecting Māori language. However, this research uses large models ‘as is’ for transfer learning, where no further training was done on Māori-English data. The best accuracy of 87% was obtained using BiLSTM with bilingual embeddings to detect Māori-English code-switching points.
Data Augmentation for Low-Resource Dialogue Summarization
Authors: Yongtai Liu, Joshua Maynez, Gonçalo Simões, Shashi Narayan
Date: 2022-07-01 00:00:00
Proceeding: Findings (NAACL) 2022 7
Tasks: Data Augmentation, Meeting Summarization

We present DADS, a novel Data Augmentation technique for low-resource Dialogue Summarization. Our method generates synthetic examples by replacing sections of text from both the input dialogue and summary while preserving the augmented summary to correspond to a viable summary for the augmented dialogue. We utilize pretrained language models that produce highly likely dialogue alternatives while still being free to generate diverse alternatives. We applied our data augmentation method to the SAMSum dataset in low resource scenarios, mimicking real world problems such as chat, thread, and meeting summarization where large scale supervised datasets with human-written summaries are scarce. Through both automatic and human evaluations, we show that DADS shows strong improvements for low resource scenarios while generating topically diverse summaries without introducing additional hallucinations to the summaries.
A Versatile Adaptive Curriculum Learning Framework for Task-oriented Dialogue Policy Learning
Authors: Yang Zhao, Hua Qin, Wang Zhenyu, Changxi Zhu, Shihan Wang
Date: 2022-07-01 00:00:00
Proceeding: Findings (NAACL) 2022 7
Tasks: Deep Reinforcement Learning

Training a deep reinforcement learning-based dialogue policy with brute-force random sampling is costly. A new training paradigm was proposed to improve learning performance and efficiency by combining curriculum learning. However, attempts in the field of dialogue policy are very limited due to the lack of reliable evaluation of difficulty scores of dialogue tasks and the high sensitivity to the mode of progression through dialogue tasks. In this paper, we present a novel versatile adaptive curriculum learning (VACL) framework, which presents a substantial step toward applying automatic curriculum learning on dialogue policy tasks. It supports evaluating the difficulty of dialogue tasks only using the learning experiences of dialogue policy and skip-level selection according to their learning needs to maximize the learning efficiency. Moreover, an attractive feature of VACL is the construction of a generic, elastic global curriculum while training a good dialogue policy that could guide different dialogue policy learning without extra effort on re-training. The superiority and versatility of VACL are validated on three public dialogue datasets.
Challenging America: Modeling language in longer time scales
Authors: Jakub Pokrywka, Filip Graliński, Krzysztof Jassem, Karol Kaczmarek, Krzysztof Jurkiewicz, Piotr Wierzchon
Date: 2022-07-01 00:00:00
Proceeding: Findings (NAACL) 2022 7
Tasks: Cloze Test, Optical Character Recognition (OCR)

The aim of the paper is to apply, for historical texts, the methodology used commonly to solve various NLP tasks defined for contemporary data, i.e. pre-train and fine-tune large Transformer models. This paper introduces an ML challenge, named Challenging America (ChallAm), based on OCR-ed excerpts from historical newspapers collected from the Chronicling America portal. ChallAm provides a dataset of clippings, labeled with metadata on their origin, and paired with their textual contents retrieved by an OCR tool. Three, publicly available, ML tasks are defined in the challenge: to determine the article date, to detect the location of the issue, and to deduce a word in a text gap (cloze test). Strong baselines are provided for all three ChallAm tasks. In particular, we pre-trained a RoBERTa model from scratch from the historical texts. We also discuss the issues of discrimination and hate-speech present in the historical American texts.
Permutation Invariant Strategy Using Transformer Encoders for Table Understanding
Authors: Sarthak Dash, Sugato Bagchi, Nandana Mihindukulasooriya, Alfio Gliozzo
Date: 2022-07-01 00:00:00
Proceeding: Findings (NAACL) 2022 7
Tasks: Column Type Annotation, Entity Linking, Question Answering, Relation Extraction, Retrieval, Semantic Retrieval

Representing text in tables is essential for many business intelligence tasks such as semantic retrieval, data exploration and visualization, and question answering. Existing methods that leverage pretrained Transformer encoders range from a simple construction of pseudo-sentences by concatenating text across rows or columns to complex parameter-intensive models that encode table structure and require additional pretraining. In this work, we introduce a novel encoding strategy for Transformer encoders that preserves the critical property of permutation invariance across rows or columns. Unlike existing state-of-the-art methods for Table Understanding, our proposed approach does not require any additional pretraining and still substantially outperforms existing methods in almost all instances. We demonstrate the effectiveness of our proposed approach on three table interpretation tasks: column type annotation, relation extraction, and entity linking through extensive experiments on existing tabular datasets.
MultiNERD: A Multilingual, Multi-Genre and Fine-Grained Dataset for Named Entity Recognition (and Disambiguation)
Authors: Simone Tedeschi, Roberto Navigli
Date: 2022-07-01 00:00:00
Proceeding: Findings (NAACL) 2022 7
Tasks: Entity Linking, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER

Named Entity Recognition (NER) is the task of identifying named entities in texts and classifying them through specific semantic categories, a process which is crucial for a wide range of NLP applications. Current datasets for NER focus mainly on coarse-grained entity types, tend to consider a single textual genre and to cover a narrow set of languages, thus limiting the general applicability of NER systems.In this work, we design a new methodology for automatically producing NER annotations, and address the aforementioned limitations by introducing a novel dataset that covers 10 languages, 15 NER categories and 2 textual genres.We also introduce a manually-annotated test set, and extensively evaluate the quality of our novel dataset on both this new test set and standard benchmarks for NER.In addition, in our dataset, we include: i) disambiguation information to enable the development of multilingual entity linking systems, and ii) image URLs to encourage the creation of multimodal systems.We release our dataset at https://github.com/Babelscape/multinerd.
Learning to Embed Multi-Modal Contexts for Situated Conversational Agents
Authors: Haeju Lee, Oh Joon Kwon, Yunseon Choi, Minho Park, Ran Han, Yoonhyung Kim, Jinhyeon Kim, Youngjune Lee, Haebin Shin, Kangwook Lee, Kee-Eung Kim
Date: 2022-07-01 00:00:00
Proceeding: Findings (NAACL) 2022 7
Tasks: coreference-resolution, Coreference Resolution, Decoder, dialog state tracking, Dialogue State Tracking, Response Generation, Retrieval

The Situated Interactive Multi-Modal Conversations (SIMMC) 2.0 aims to create virtual shopping assistants that can accept complex multi-modal inputs, i.e. visual appearances of objects and user utterances. It consists of four subtasks, multi-modal disambiguation (MM-Disamb), multi-modal coreference resolution (MM-Coref), multi-modal dialog state tracking (MM-DST), and response retrieval and generation. While many task-oriented dialog systems usually tackle each subtask separately, we propose a jointly learned multi-modal encoder-decoder that incorporates visual inputs and performs all four subtasks at once for efficiency. This approach won the MM-Coref and response retrieval subtasks and nominated runner-up for the remaining subtasks using a single unified model at the 10th Dialog Systems Technology Challenge (DSTC10), setting a high bar for the novel task of multi-modal task-oriented dialog systems.
Empathetic Persuasion: Reinforcing Empathy and Persuasiveness in Dialogue Systems
Authors: Azlaan Mustafa Samad, Kshitij Mishra, Mauajama Firdaus, Asif Ekbal
Date: 2022-07-01 00:00:00
Proceeding: Findings (NAACL) 2022 7
Tasks: Language Modeling, Language Modelling, Persuasiveness, Reinforcement Learning (RL)

Persuasion is an intricate process involving empathetic connection between two individuals. Plain persuasive responses may make a conversation non-engaging. Even the most well-intended and reasoned persuasive conversations can fall through in the absence of empathetic connection between the speaker and listener. In this paper, we propose a novel task of incorporating empathy when generating persuasive responses. We develop an empathetic persuasive dialogue system by fine-tuning a maximum likelihood Estimation (MLE)-based language model in a reinforcement learning (RL) framework. To design feedbacks for our RL-agent, we define an effective and efficient reward function considering consistency, repetitiveness, emotion and persuasion rewards to ensure consistency, non-repetitiveness, empathy and persuasiveness in the generated responses. Due to lack of emotion annotated persuasive data, we first annotate the existing Persuaion For Good dataset with emotions, then build transformer based classifiers to provide emotion based feedbacks to our RL agent. Experimental results confirm that our proposed model increases the rate of generating persuasive responses as compared to the available state-of-the-art dialogue models while making the dialogues empathetically more engaging and retaining the language quality in responses.