pwc-archive/papers-with-abstracts huggingface dataset:
LITE: Intent-based Task Representation Learning Using Weak Supervision
Authors: Naoki Otani, Michael Gamon, Sujay Kumar Jauhar, Mei Yang, Sri Raghu Malireddi, Oriana Riva
Date: 2022-07-01 00:00:00
Proceeding: NAACL 2022 7
Tasks: Management, Multi-Task Learning, Representation Learning

Users write to-dos as personal notes to themselves, about things they need to complete, remember or organize. To-do texts are usually short and under-specified, which poses a challenge for current text representation models. Yet, understanding and representing their meaning is the first step towards providing intelligent assistance for to-do management. We address this problem by proposing a neural multi-task learning framework, LITE, which extracts representations of English to-do tasks with a multi-head attention mechanism on top of a pre-trained text encoder. To adapt representation models to to-do texts, we collect weak-supervision labels from semantically rich external resources (e.g., dynamic commonsense knowledge bases), following the principle that to-do tasks with similar intents have similar labels. We then train the model on multiple generative/predictive training objectives jointly. We evaluate our representation model on four downstream tasks and show that our approach consistently improves performance over baseline models, achieving error reduction of up to 38.7%.
A Shoulder to Cry on: Towards A Motivational Virtual Assistant for Assuaging Mental Agony
Authors: Tulika Saha, Saichethan Reddy, Anindya Das, Sriparna Saha, Pushpak Bhattacharyya
Date: 2022-07-01 00:00:00
Proceeding: NAACL 2022 7
Tasks: Dialogue Generation, Reinforcement Learning (RL)

Mental Health Disorders continue plaguing humans worldwide. Aggravating this situation is the severe shortage of qualified and competent mental health professionals (MHPs), which underlines the need for developing Virtual Assistants (VAs) that can assist MHPs. The data+ML for automation can come from platforms that allow visiting and posting messages in peer-to-peer anonymous manner for sharing their experiences (frequently stigmatized) and seeking support. In this paper, we propose a VA that can act as the first point of contact and comfort for mental health patients. We curate a dataset, Motivational VA: MotiVAte comprising of 7k dyadic conversations collected from a peer-to-peer support platform. The system employs two mechanisms: (i) Mental Illness Classification: an attention based BERT classifier that outputs the mental disorder category out of the 4 categories, viz., Major Depressive Disorder (MDD), Anxiety, Obsessive Compulsive Disorder (OCD) and Post-traumatic Stress Disorder (PTSD), based on the input ongoing dialog between the support seeker and the VA; and (ii) Mental Illness Conditioned Motivational Dialogue Generation (MI-MDG): a sentiment driven Reinforcement Learning (RL) based motivational response generator. The empirical evaluation demonstrates the system capability by way of outperforming several baselines.
Cheat Codes to Quantify Missing Source Information in Neural Machine Translation
Authors: Proyag Pal, Kenneth Heafield
Date: 2022-07-01 00:00:00
Proceeding: NAACL 2022 7
Tasks: Machine Translation, Sentence, Translation

This paper describes a method to quantify the amount of information H(t|s) added by the target sentence t that is not present in the source s in a neural machine translation system. We do this by providing the model the target sentence in a highly compressed form (a “cheat code”), and exploring the effect of the size of the cheat code. We find that the model is able to capture extra information from just a single float representation of the target and nearly reproduces the target with two 32-bit floats per target token.
Text Style Transfer via Optimal Transport
Authors: Nasim Nouri
Date: 2022-07-01 00:00:00
Proceeding: NAACL 2022 7
Tasks: Style Transfer, Text Style Transfer

Text style transfer (TST) is a well-known task whose goal is to convert the style of the text (e.g., from formal to informal) while preserving its content. Recently, it has been shown that both syntactic and semantic similarities between the source and the converted text are important for TST. However, the interaction between these two concepts has not been modeled. In this work, we propose a novel method based on Optimal Transport for TST to simultaneously incorporate syntactic and semantic information into similarity computation between the source and the converted text. We evaluate the proposed method in both supervised and unsupervised settings. Our analysis reveal the superiority of the proposed model in both settings.
Interactive Query-Assisted Summarization via Deep Reinforcement Learning
Authors: Ori Shapira, Ramakanth Pasunuru, Mohit Bansal, Ido Dagan, Yael Amsterdamer
Date: 2022-07-01 00:00:00
Proceeding: NAACL 2022 7
Tasks: Deep Reinforcement Learning, Informativeness, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Interactive summarization is a task that facilitates user-guided exploration of information within a document set. While one would like to employ state of the art neural models to improve the quality of interactive summarization, many such technologies cannot ingest the full document set or cannot operate at sufficient speed for interactivity. To that end, we propose two novel deep reinforcement learning models for the task that address, respectively, the subtask of summarizing salient information that adheres to user queries, and the subtask of listing suggested queries to assist users throughout their exploration. In particular, our models allow encoding the interactive session state and history to refrain from redundancy. Together, these models compose a state of the art solution that addresses all of the task requirements. We compare our solution to a recent interactive summarization system, and show through an experimental study involving real users that our models are able to improve informativeness while preserving positive user experience.
Data Augmentation with Dual Training for Offensive Span Detection
Authors: Nasim Nouri
Date: 2022-07-01 00:00:00
Proceeding: NAACL 2022 7
Tasks: Data Augmentation, Language Modeling, Language Modelling, Management, text-classification, Text Classification

Recognizing offensive text is an important requirement for every content management system, especially for social networks. While the majority of the prior work formulate this problem as text classification, i.e., if a text excerpt is offensive or not, in this work we propose a novel model for offensive span detection (OSD), whose goal is to identify the spans responsible for the offensive tone of the text. One of the challenges to train a model for this novel setting is the lack of enough training data. To address this limitation, in this work we propose a novel method in which the large-scale pre-trained language model GPT-2 is employed to generate synthetic training data for OSD. In particular, we propose to train the GPT-2 model in a dual-training setting using the REINFORCE algorithm to generate in-domain, natural and diverse training samples. Extensive experiments on the benchmark dataset for OSD reveal the effectiveness of the proposed method.
Don’t sweat the small stuff, classify the rest: Sample Shielding to protect text classifiers against adversarial attacks
Authors: Jonathan Rusert, Padmini Srinivasan
Date: 2022-07-01 00:00:00
Proceeding: NAACL 2022 7
Tasks: text-classification, Text Classification

Deep learning (DL) is being used extensively for text classification. However, researchers have demonstrated the vulnerability of such classifiers to adversarial attacks. Attackers modify the text in a way which misleads the classifier while keeping the original meaning close to intact. State-of-the-art (SOTA) attack algorithms follow the general principle of making minimal changes to the text so as to not jeopardize semantics. Taking advantage of this we propose a novel and intuitive defense strategy called Sample Shielding.It is attacker and classifier agnostic, does not require any reconfiguration of the classifier or external resources and is simple to implement. Essentially, we sample subsets of the input text, classify them and summarize these into a final decision. We shield three popular DL text classifiers with Sample Shielding, test their resilience against four SOTA attackers across three datasets in a realistic threat setting. Even when given the advantage of knowing about our shielding strategy the adversary’s attack success rate is <=10% with only one exception and often < 5%. Additionally, Sample Shielding maintains near original accuracy when applied to original texts. Crucially, we show that the ‘make minimal changes’ approach of SOTA attackers leads to critical vulnerabilities that can be defended against with an intuitive sampling strategy.
Dynamic Gazetteer Integration in Multilingual Models for Cross-Lingual and Cross-Domain Named Entity Recognition
Authors: Besnik Fetahu, Anjie Fang, Oleg Rokhlenko, Shervin Malmasi
Date: 2022-07-01 00:00:00
Proceeding: NAACL 2022 7
Tasks: Cross-Domain Named Entity Recognition, Cross-Lingual Transfer, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER, Transfer Learning

Named entity recognition (NER) in a real-world setting remains challenging and is impacted by factors like text genre, corpus quality, and data availability. NER models trained on CoNLL do not transfer well to other domains, even within the same language. This is especially the case for multi-lingual models when applied to low-resource languages, and is mainly due to missing entity information. We propose an approach that with limited effort and data, addresses the NER knowledge gap across languages and domains. Our novel approach uses a token-level gating layer to augment pre-trained multilingual transformers with gazetteers containing named entities (NE) from a target language or domain.This approach provides the flexibility to jointly integrate both textual and gazetteer information dynamically: entity knowledge from gazetteers is used only when a token’s textual representation is insufficient for the NER task.Evaluation on several languages and domains demonstrates: (i) a high mismatch of reported NER performance on CoNLL vs. domain specific datasets, (ii) gazetteers significantly improve NER performance across languages and domains, and (iii) gazetteers can be flexibly incorporated to guide knowledge transfer. On cross-lingual transfer we achieve an improvement over the baseline with F1=+17.6%, and with F1=+21.3% for cross-domain transfer.
Enhancing Knowledge Selection for Grounded Dialogues via Document Semantic Graphs
Authors: Sha Li, Mahdi Namazifar, Di Jin, Mohit Bansal, Heng Ji, Yang Liu, Dilek Hakkani-Tur
Date: 2022-07-01 00:00:00
Proceeding: NAACL 2022 7
Tasks: Multi-Task Learning, Response Generation, Sentence

Providing conversation models with background knowledge has been shown to make open-domain dialogues more informative and engaging. Existing models treat knowledge selection as a sentence ranking or classification problem where each sentence is handled individually, ignoring the internal semantic connection between sentences. In this work, we propose to automatically convert the background knowledge documents into document semantic graphs and then perform knowledge selection over such graphs. Our document semantic graphs preserve sentence-level information through the use of sentence nodes and provide concept connections between sentences. We apply multi-task learning to perform sentence-level knowledge selection and concept-level knowledge selection, showing that it improves sentence-level selection. Our experiments show that our semantic graph-based knowledge selection improves over sentence selection baselines for both the knowledge selection task and the end-to-end response generation task on HollE and improves generalization on unseen topics in WoW.
Using Natural Sentence Prompts for Understanding Biases in Language Models
Authors: Sarah Alnegheimish, Alicia Guo, Yi Sun
Date: 2022-07-01 00:00:00
Proceeding: NAACL 2022 7
Tasks: Sentence

Evaluation of biases in language models is often limited to synthetically generated datasets. This dependence traces back to the need of prompt-style dataset to trigger specific behaviors of language models. In this paper, we address this gap by creating a prompt dataset with respect to occupations collected from real-world natural sentences present in Wikipedia.We aim to understand the differences between using template-based prompts and natural sentence prompts when studying gender-occupation biases in language models. We find bias evaluations are very sensitiveto the design choices of template prompts, and we propose using natural sentence prompts as a way of more systematically using real-world sentences to move away from design decisions that may bias the results.
The USMLE® Step 2 Clinical Skills Patient Note Corpus
Authors: Victoria Yaneva, Janet Mee, Le Ha, Polina Harik, Michael Jodoin, Alex Mechaber
Date: 2022-07-01 00:00:00
Proceeding: NAACL 2022 7

This paper presents a corpus of 43,985 clinical patient notes (PNs) written by 35,156 examinees during the high-stakes USMLE® Step 2 Clinical Skills examination. In this exam, examinees interact with standardized patients - people trained to portray simulated scenarios called clinical cases. For each encounter, an examinee writes a PN, which is then scored by physician raters using a rubric of clinical concepts, expressions of which should be present in the PN. The corpus features PNs from 10 clinical cases, as well as the clinical concepts from the case rubrics. A subset of 2,840 PNs were annotated by 10 physician experts such that all 143 concepts from the case rubrics (e.g., shortness of breath) were mapped to 34,660 PN phrases (e.g., dyspnea, difficulty breathing). The corpus is available via a data sharing agreement with NBME and can be requested at https://www.nbme.org/services/data-sharing.
Learning to Borrow– Relation Representation for Without-Mention Entity-Pairs for Knowledge Graph Completion
Authors: Huda Hakami, Mona Hakami, Angrosh Mandya, Danushka Bollegala
Date: 2022-07-01 00:00:00
Proceeding: NAACL 2022 7
Tasks: Entity Embeddings, Graph Embedding, Knowledge Graph Completion, Knowledge Graph Embedding, Knowledge Graphs, Link Prediction, Relation, Sentence

Prior work on integrating text corpora with knowledge graphs (KGs) to improve Knowledge Graph Embedding (KGE) have obtained good performance for entities that co-occur in sentences in text corpora. Such sentences (textual mentions of entity-pairs) are represented as Lexicalised Dependency Paths (LDPs) between two entities. However, it is not possible to represent relations between entities that do not co-occur in a single sentence using LDPs. In this paper, we propose and evaluate several methods to address this problem, where we borrow LDPs from the entity pairs that co-occur in sentences in the corpus (i.e. with mentions entity pairs) to represent entity pairs that do not co-occur in any sentence in the corpus (i.e. without mention entity pairs). We propose a supervised borrowing method, SuperBorrow, that learns to score the suitability of an LDP to represent a without-mentions entity pair using pre-trained entity embeddings and contextualised LDP representations. Experimental results show that SuperBorrow improves the link prediction performance of multiple widely-used prior KGE methods such as TransE, DistMult, ComplEx and RotatE.
Modal Dependency Parsing via Language Model Priming
Authors: Jiarui Yao, Nianwen Xue, Bonan Min
Date: 2022-07-01 00:00:00
Proceeding: NAACL 2022 7
Tasks: Dependency Parsing, Language Modeling, Language Modelling, model

The task of modal dependency parsing aims to parse a text into its modal dependency structure, which is a representation for the factuality of events in the text. We design a modal dependency parser that is based on priming pre-trained language models, and evaluate the parser on two data sets. Compared to baselines, we show an improvement of 2.6% in F-score for English and 4.6% for Chinese. To the best of our knowledge, this is also the first work on Chinese modal dependency parsing.
Incorporating Centering Theory into Neural Coreference Resolution
Authors: Haixia Chai, Michael Strube
Date: 2022-07-01 00:00:00
Proceeding: NAACL 2022 7
Tasks: coreference-resolution, Coreference Resolution, Open-Ended Question Answering

In recent years, transformer-based coreference resolution systems have achieved remarkable improvements on the CoNLL dataset. However, how coreference resolvers can benefit from discourse coherence is still an open question. In this paper, we propose to incorporate centering transitions derived from centering theory in the form of a graph into a neural coreference model. Our method improves the performance over the SOTA baselines, especially on pronoun resolution in long documents, formal well-structured text, and clusters with scattered mentions.
Features or Spurious Artifacts? Data-centric Baselines for Fair and Robust Hate Speech Detection
Authors: Alan Ramponi, Sara Tonelli
Date: 2022-07-01 00:00:00
Proceeding: NAACL 2022 7
Tasks: Fairness, Hate Speech Detection

Avoiding to rely on dataset artifacts to predict hate speech is at the cornerstone of robust and fair hate speech detection. In this paper we critically analyze lexical biases in hate speech detection via a cross-platform study, disentangling various types of spurious and authentic artifacts and analyzing their impact on out-of-distribution fairness and robustness. We experiment with existing approaches and propose simple yet surprisingly effective data-centric baselines. Our results on English data across four platforms show that distinct spurious artifacts require different treatments to ultimately attain both robustness and fairness in hate speech detection. To encourage research in this direction, we release all baseline models and the code to compute artifacts, pointing it out as a complementary and necessary addition to the data statements practice.
Document-Level Event Argument Extraction by Leveraging Redundant Information and Closed Boundary Loss
Authors: Hanzhang Zhou, Kezhi Mao
Date: 2022-07-01 00:00:00
Proceeding: NAACL 2022 7
Tasks: Event Argument Extraction

In document-level event argument extraction, an argument is likely to appear multiple times in different expressions in the document. The redundancy of arguments underlying multiple sentences is beneficial but is often overlooked. In addition, in event argument extraction, most entities are regarded as class “others”, i.e. Universum class, which is defined as a collection of samples that do not belong to any class of interest. Universum class is composed of heterogeneous entities without typical common features. Classifiers trained by cross entropy loss could easily misclassify the Universum class because of their open decision boundary. In this paper, to make use of redundant event information underlying a document, we build an entity coreference graph with the graph2token module to produce a comprehensive and coreference-aware representation for every entity and then build an entity summary graph to merge the multiple extraction results. To better classify Universum class, we propose a new loss function to build classifiers with closed boundaries. Experimental results show that our model outperforms the previous state-of-the-art models by 3.35% in F1-score.
Efficient Constituency Tree based Encoding for Natural Language to Bash Translation
Authors: Shikhar Bharadwaj, Shirish Shevade
Date: 2022-07-01 00:00:00
Proceeding: NAACL 2022 7
Tasks: CPU, GPU, Translation

Bash is a Unix command language used for interacting with the Operating System. Recent works on natural language to Bash translation have made significant advances, but none of the previous methods utilize the problem’s inherent structure. We identify this structure andpropose a Segmented Invocation Transformer (SIT) that utilizes the information from the constituency parse tree of the natural language text. Our method is motivated by the alignment between segments in the natural language text and Bash command components. Incorporating the structure in the modelling improves the performance of the model. Since such systems must be universally accessible, we benchmark the inference times on a CPU rather than a GPU. We observe a 1.8x improvement in the inference time and a 5x reduction in model parameters. Attribution analysis using Integrated Gradients reveals that the proposed method can capture the problem structure.
Unsupervised Paraphrasability Prediction for Compound Nominalizations
Authors: John Sie Yuen Lee, Ho Hung Lim, Carol Carol Webster
Date: 2022-07-01 00:00:00
Proceeding: NAACL 2022 7
Tasks: Abstract Meaning Representation, Language Modeling, Language Modelling, Paraphrase Generation, Prediction

Commonly found in academic and formal texts, a nominalization uses a deverbal noun to describe an event associated with its corresponding verb. Nominalizations can be difficult to interpret because of ambiguous semantic relations between the deverbal noun and its arguments. Automatic generation of clausal paraphrases for nominalizations can help disambiguate their meaning. However, previous work has not identified cases where it is awkward or impossible to paraphrase a compound nominalization. This paper investigates unsupervised prediction of paraphrasability, which determines whether the prenominal modifier of a nominalization can be re-written as a noun or adverb in a clausal paraphrase. We adopt the approach of overgenerating candidate paraphrases followed by candidate ranking with a neural language model. In experiments on an English dataset, we show that features from an Abstract Meaning Representation graph lead to statistically significant improvement in both paraphrasability prediction and paraphrase generation.
Crossroads, Buildings and Neighborhoods: A Dataset for Fine-grained Location Recognition
Authors: Pei Chen, Haotian Xu, Cheng Zhang, Ruihong Huang
Date: 2022-07-01 00:00:00
Proceeding: NAACL 2022 7
Tasks: named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER

General domain Named Entity Recognition (NER) datasets like CoNLL-2003 mostly annotate coarse-grained location entities such as a country or a city. But many applications require identifying fine-grained locations from texts and mapping them precisely to geographic sites, e.g., a crossroad, an apartment building, or a grocery store. In this paper, we introduce a new dataset HarveyNER with fine-grained locations annotated in tweets. This dataset presents unique challenges and characterizes many complex and long location mentions in informal descriptions. We built strong baseline models using Curriculum Learning and experimented with different heuristic curricula to better recognize difficult location mentions. Experimental results show that the simple curricula can improve the system’s performance on hard cases and its overall performance, and outperform several other baseline systems. The dataset and the baseline models can be found at https://github.com/brickee/HarveyNER.