pwc-archive/papers-with-abstracts huggingface dataset:
A New Surprise Measure for Extracting Interesting Relationships between Persons
Authors: Hidetaka Kamigaito, Jingun Kwon, Young-In Song, Manabu Okumura
Date: 2021-04-01 00:00:00
Proceeding: EACL 2021 2
Tasks: Articles

One way to enhance user engagement in search engines is to suggest interesting facts to the user. Although relationships between persons are important as a target for text mining, there are few effective approaches for extracting the interesting relationships between persons. We therefore propose a method for extracting interesting relationships between persons from natural language texts by focusing on their surprisingness. Our method first extracts all personal relationships from dependency trees for the texts and then calculates surprise scores for distributed representations of the extracted relationships in an unsupervised manner. The unique point of our method is that it does not require any labeled dataset with annotation for the surprising personal relationships. The results of the human evaluation show that the proposed method could extract more interesting relationships between persons from Japanese Wikipedia articles than a popularity-based baseline method. We demonstrate our proposed method as a chrome plugin on google search.
Paladin: an annotation tool based on active and proactive learning
Authors: Minh-Quoc Nghiem, Paul Baylis, Sophia Ananiadou
Date: 2021-04-01 00:00:00
Proceeding: EACL 2021 2
Tasks: Active Learning

In this paper, we present Paladin, an open-source web-based annotation tool for creating high-quality multi-label document-level datasets. By integrating active learning and proactive learning to the annotation task, Paladin makes the task less time-consuming and requiring less human effort. Although Paladin is designed for multi-label settings, the system is flexible and can be adapted to other tasks in single-label settings.
Story Centaur: Large Language Model Few Shot Learning as a Creative Writing Tool
Authors: Ben Swanson, Kory Mathewson, Ben Pietrzak, Sherol Chen, Monica Dinalescu
Date: 2021-04-01 00:00:00
Proceeding: EACL 2021 2
Tasks: Few-Shot Learning, Language Modeling, Language Modelling, Large Language Model

Few shot learning with large language models has the potential to give individuals without formal machine learning training the access to a wide range of text to text models. We consider how this applies to creative writers and present Story Centaur, a user interface for prototyping few shot models and a set of recombinable web components that deploy them. Story Centaur{'}s goal is to expose creative writers to few shot learning with a simple but powerful interface that lets them compose their own co-creation tools that further their own unique artistic directions. We build out several examples of such tools, and in the process probe the boundaries and issues surrounding generation with large language models.
FrameForm: An Open-source Annotation Interface for FrameNet
Authors: B{\"u}{\c{s}}ra Mar{\c{s}}an, Olcay Taner Y{\i}ld{\i}z
Date: 2021-04-01 00:00:00
Proceeding: EACL 2021 2

In this paper, we introduce FrameForm, an open-source annotation tool designed to accommodate predicate annotations based on Frame Semantics. FrameForm is a user-friendly tool for creating, annotating and maintaining computational lexicography projects like FrameNet and has been used while building the Turkish FrameNet. Responsive and open-source, FrameForm can be easily modified to answer the annotation needs of a wide range of different languages.
ELITR Multilingual Live Subtitling: Demo and Strategy
Authors: Ond{\v{r}}ej Bojar, Dominik Mach{\'a}{\v{c}}ek, Sangeet Sagar, Otakar Smr{\v{z}}, Jon{\'a}{\v{s}} Kratochv{\'\i}l, Peter Pol{\'a}k, Ebrahim Ansari, Mohammad Mahmoudi, Rishu Kumar, Dario Franceschini, Chiara Canton, Ivan Simonini, Thai-Son Nguyen, Felix Schneider, Sebastian St{\"u}ker, Alex Waibel, Barry Haddow, Rico Sennrich, Philip Williams
Date: 2021-04-01 00:00:00
Proceeding: EACL 2021 2
Tasks: automatic-speech-translation, Translation

This paper presents an automatic speech translation system aimed at live subtitling of conference presentations. We describe the overall architecture and key processing components. More importantly, we explain our strategy for building a complex system for end-users from numerous individual components, each of which has been tested only in laboratory conditions. The system is a working prototype that is routinely tested in recognizing English, Czech, and German speech and presenting it translated simultaneously into 42 target languages.
OPUS-CAT: Desktop NMT with CAT integration and local fine-tuning
Authors: Tommi Nieminen
Date: 2021-04-01 00:00:00
Proceeding: EACL 2021 2
Tasks: Machine Translation, NMT, Translation

OPUS-CAT is a collection of software which enables translators to use neural machine translation in computer-assisted translation tools without exposing themselves to security and confidentiality risks inherent in online machine translation. OPUS-CAT uses the public OPUS-MT machine translation models, which are available for over a thousand language pairs. The generic OPUS-MT models can be fine-tuned with OPUS-CAT on the desktop using data for a specific client or domain.
Domain Expert Platform for Goal-Oriented Dialog Collection
Authors: Didzis Go{\v{s}}ko, Arturs Znotins, Inguna Skadina, Normunds Gruzitis, Gunta Ne{\v{s}}pore-B{\=e}rzkalne
Date: 2021-04-01 00:00:00
Proceeding: EACL 2021 2
Tasks: Goal-Oriented Dialog

Today, most dialogue systems are fully or partly built using neural network architectures. A crucial prerequisite for the creation of a goal-oriented neural network dialogue system is a dataset that represents typical dialogue scenarios and includes various semantic annotations, e.g. intents, slots and dialogue actions, that are necessary for training a particular neural network architecture. In this demonstration paper, we present an easy to use interface and its back-end which is oriented to domain experts for the collection of goal-oriented dialogue samples. The platform not only allows to collect or write sample dialogues in a structured way, but also provides a means for simple annotation and interpretation of the dialogues. The platform itself is language-independent; it depends only on the availability of particular language processing components for a specific language. It is currently being used to collect dialogue samples in Latvian (a highly inflected language) which represent typical communication between students and the student service.
Which is Better for Deep Learning: Python or MATLAB? Answering Comparative Questions in Natural Language
Authors: Viktoriia Chekalina, Alexander Bondarenko, Chris Biemann, Meriem Beloucif, Varvara Logacheva, Alexander Panchenko
Date: 2021-04-01 00:00:00
Proceeding: EACL 2021 2

We present a system for answering comparative questions (Is X better than Y with respect to Z?) in natural language. Answering such questions is important for assisting humans in making informed decisions. The key component of our system is a natural language interface for comparative QA that can be used in personal assistants, chatbots, and similar NLP devices. Comparative QA is a challenging NLP task, since it requires collecting support evidence from many different sources, and direct comparisons of rare objects may be not available even on the entire Web. We take the first step towards a solution for such a task offering a testbed for comparative QA in natural language by probing several methods, making the three best ones available as an online demo.
PunKtuator: A Multilingual Punctuation Restoration System for Spoken and Written Text
Authors: Varnith Chordia
Date: 2021-04-01 00:00:00
Proceeding: EACL 2021 2
Tasks: Language Modelling, Punctuation Restoration, Sentence, Translation

Text transcripts without punctuation or sentence boundaries are hard to comprehend for both humans and machines. Punctuation marks play a vital role by providing meaning to the sentence and incorrect use or placement of punctuation marks can often alter it. This can impact downstream tasks such as language translation and understanding, pronoun resolution, text summarization, etc. for humans and machines. An automated punctuation restoration (APR) system with minimal human intervention can improve comprehension of text and help users write better. In this paper we describe a multitask modeling approach as a system to restore punctuation in multiple high resource {--} Germanic (English and German), Romanic (French){--} and low resource languages {--} Indo-Aryan (Hindi) Dravidian (Tamil) {--} that does not require extensive knowledge of grammar or syntax of a given language for both spoken and written form of text. For German language and the given Indic based languages this is the first towards restoring punctuation and can serve as a baseline for future work.
Conversational Agent for Daily Living Assessment Coaching Demo
Authors: Raymond Finzel, Aditya Gaydhani, Sheena Dufresne, Maria Gini, Serguei Pakhomov
Date: 2021-04-01 00:00:00
Proceeding: EACL 2021 2
Tasks: Navigate, Text Generation

Conversational Agent for Daily Living Assessment Coaching (CADLAC) is a multi-modal conversational agent system designed to impersonate {``}individuals{''} with various levels of ability in activities of daily living (ADLs: e.g., dressing, bathing, mobility, etc.) for use in training professional assessors how to conduct interviews to determine one{'}s level of functioning. The system is implemented on the MindMeld platform for conversational AI and features a Bidirectional Long Short-Term Memory topic tracker that allows the agent to navigate conversations spanning 18 different ADL domains, a dialogue manager that interfaces with a database of over 10,000 historical ADL assessments, a rule-based Natural Language Generation (NLG) module, and a pre-trained open-domain conversational sub-agent (based on GPT-2) for handling conversation turns outside of the 18 ADL domains. CADLAC is delivered via state-of-the-art web frameworks to handle multiple conversations and users simultaneously and is enabled with voice interface. The paper includes a description of the system design and evaluation of individual components followed by a brief discussion of current limitations and next steps.
Improving Medical NLI Using Context-Aware Domain Knowledge
Authors: Shaika Chowdhury, Philip Yu, Yuan Luo
Date: 2020-12-01 00:00:00
Proceeding: Joint Conference on Lexical and Computational Semantics 2020
Tasks: Natural Language Inference

Domain knowledge is important to understand both the lexical and relational associations of words in natural language text, especially for domain-specific tasks like Natural Language Inference (NLI) in the medical domain, where due to the lack of a large annotated dataset such knowledge cannot be implicitly learned during training. However, because of the linguistic idiosyncrasies of clinical texts (e.g., shorthand jargon), solely relying on domain knowledge from an external knowledge base (e.g., UMLS) can lead to wrong inference predictions as it disregards contextual information and, hence, does not return the most relevant mapping. To remedy this, we devise a knowledge adaptive approach for medical NLI that encodes the premise/hypothesis texts by leveraging supplementary external knowledge, alongside the UMLS, based on the word contexts. By incorporating refined domain knowledge at both the lexical and relational levels through a multi-source attention mechanism, it is able to align the token-level interactions between the premise and hypothesis more effectively. Comprehensive experiments and case study on the recently released MedNLI dataset are conducted to validate the effectiveness of the proposed approach.
Reading Comprehension as Natural Language Inference:A Semantic Analysis
Authors: Anshuman Mishra, Dhruvesh Patel, Aparna Vijayakumar, Xiang Li, Pavan Kapanipathi, Kartik Talamadupula
Date: 2020-12-01 00:00:00
Proceeding: Joint Conference on Lexical and Computational Semantics 2020
Tasks: Form, Natural Language Inference, Question Answering, Reading Comprehension

In the recent past, Natural language Inference (NLI) has gained significant attention, particularly given its promise for downstream NLP tasks. However, its true impact is limited and has not been well studied. Therefore, in this paper, we explore the utility of NLI for one of the most prominent downstream tasks, viz. Question Answering (QA). We transform one of the largest available MRC dataset (RACE) to an NLI form, and compare the performances of a state-of-the-art model (RoBERTa) on both these forms. We propose new characterizations of questions, and evaluate the performance of QA and NLI models on these categories. We highlight clear categories for which the model is able to perform better when the data is presented in a coherent entailment form, and a structured question-answer concatenation form, respectively.
Automatic Learning of Modality Exclusivity Norms with Crosslingual Word Embeddings
Authors: Emmanuele Chersoni, Rong Xiang, Qin Lu, Chu-Ren Huang
Date: 2020-12-01 00:00:00
Proceeding: Joint Conference on Lexical and Computational Semantics 2020
Tasks: Word Embeddings

Collecting modality exclusivity norms for lexical items has recently become a common practice in psycholinguistics and cognitive research. However, these norms are available only for a relatively small number of languages and often involve a costly and time-consuming collection of ratings. In this work, we aim at learning a mapping between word embeddings and modality norms. Our experiments focused on crosslingual word embeddings, in order to predict modality association scores by training on a high-resource language and testing on a low-resource one. We ran two experiments, one in a monolingual and the other one in a crosslingual setting. Results show that modality prediction using off-the-shelf crosslingual embeddings indeed has moderate-to-high correlations with human ratings even when regression algorithms are trained on an English resource and tested on a completely unseen language.
Joint Training for Learning Cross-lingual Embeddings with Sub-word Information without Parallel Corpora
Authors: Ali Hakimi Parizi, Paul Cook
Date: 2020-12-01 00:00:00
Proceeding: Joint Conference on Lexical and Computational Semantics 2020
Tasks: Bilingual Lexicon Induction, Classification, Cross-Lingual Word Embeddings, Document Classification, Word Embeddings, Word Similarity

In this paper, we propose a novel method for learning cross-lingual word embeddings, that incorporates sub-word information during training, and is able to learn high-quality embeddings from modest amounts of monolingual data and a bilingual lexicon. This method could be particularly well-suited to learning cross-lingual embeddings for lower-resource, morphologically-rich languages, enabling knowledge to be transferred from rich- to lower-resource languages. We evaluate our proposed approach simulating lower-resource languages for bilingual lexicon induction, monolingual word similarity, and document classification. Our results indicate that incorporating sub-word information indeed leads to improvements, and in the case of document classification, performance better than, or on par with, strong benchmark approaches.
Semantic Structural Decomposition for Neural Machine Translation
Authors: Elior Sulem, Omri Abend, Ari Rappoport
Date: 2020-12-01 00:00:00
Proceeding: Joint Conference on Lexical and Computational Semantics 2020
Tasks: Machine Translation, Semantic Parsing, Sentence, Text Simplification, Translation

Building on recent advances in semantic parsing and text simplification, we investigate the use of semantic splitting of the source sentence as preprocessing for machine translation. We experiment with a Transformer model and evaluate using large-scale crowd-sourcing experiments. Results show a significant increase in fluency on long sentences on an English-to- French setting with a training corpus of 5M sentence pairs, while retaining comparable adequacy. We also perform a manual analysis which explores the tradeoff between adequacy and fluency in the case where all sentence lengths are considered.
Knowledge Graphs meet Moral Values
Authors: Ioana Hulpu{\textcommabelow{s}}, Jonathan Kobbe, Heiner Stuckenschmidt, Graeme Hirst
Date: 2020-12-01 00:00:00
Proceeding: Joint Conference on Lexical and Computational Semantics 2020
Tasks: Knowledge Graphs

Operationalizing morality is crucial for understanding multiple aspects of society that have moral values at their core {--} such as riots, mobilizing movements, public debates, etc. Moral Foundations Theory (MFT) has become one of the most adopted theories of morality partly due to its accompanying lexicon, the Moral Foundation Dictionary (MFD), which offers a base for computationally dealing with morality. In this work, we exploit the MFD in a novel direction by investigating how well moral values are captured by KGs. We explore three widely used KGs, and provide concept-level analogues for the MFD. Furthermore, we propose several Personalized PageRank variations in order to score all the concepts and entities in the KGs with respect to their relevance to the different moral values. Our promising results help to progress the operationalization of morality in both NLP and KG communities.
On the Systematicity of Probing Contextualized Word Representations: The Case of Hypernymy in BERT
Authors: Abhilasha Ravichander, Eduard Hovy, Kaheer Suleman, Adam Trischler, Jackie Chi Kit Cheung
Date: 2020-12-01 00:00:00
Proceeding: Joint Conference on Lexical and Computational Semantics 2020

Contextualized word representations have become a driving force in NLP, motivating widespread interest in understanding their capabilities and the mechanisms by which they operate. Particularly intriguing is their ability to identify and encode conceptual abstractions. Past work has probed BERT representations for this competence, finding that BERT can correctly retrieve noun hypernyms in cloze tasks. In this work, we ask the question: \textit{do probing studies shed light on systematic knowledge in BERT representations?} As a case study, we examine hypernymy knowledge encoded in BERT representations. In particular, we demonstrate through a simple consistency probe that the ability to correctly retrieve hypernyms in cloze tasks, as used in prior work, does not correspond to systematic knowledge in BERT. Our main conclusion is cautionary: even if BERT demonstrates high probing accuracy for a particular competence, it does not necessarily follow that BERT {`}understands{'} a concept, and it cannot be expected to systematically generalize across applicable contexts.
Assessing Polyseme Sense Similarity through Co-predication Acceptability and Contextualised Embedding Distance
Authors: Janosch Haber, Massimo Poesio
Date: 2020-12-01 00:00:00
Proceeding: Joint Conference on Lexical and Computational Semantics 2020
Tasks: Word Embeddings

Co-predication is one of the most frequently used linguistic tests to tell apart shifts in polysemic sense from changes in homonymic meaning. It is increasingly coming under criticism as evidence is accumulating that it tends to mis-classify specific cases of polysemic sense alteration as homonymy. In this paper, we collect empirical data to investigate these accusations. We asses how co-predication acceptability relates to explicit ratings of polyseme word sense similarity, and how well either measure can be predicted through the distance between target words{'} contextualised word embeddings. We find that sense similarity appears to be a major contributor in determining co-predication acceptability, but that co-predication judgements tend to rate especially less similar sense interpretations equally as unacceptable as homonym pairs, effectively mis-classifying these instances. The tested contextualised word embeddings fail to predict word sense similarity consistently, but the similarities between BERT embeddings show a significant correlation with co-predication ratings. We take this finding as evidence that BERT embeddings might be better representations of context than encodings of word meaning.
Fine-tuning BERT with Focus Words for Explanation Regeneration
Authors: Isaiah Onando Mulang{'}, Jennifer D{'}Souza, S{\"o}ren Auer
Date: 2020-12-01 00:00:00
Proceeding: Joint Conference on Lexical and Computational Semantics 2020
Tasks: Explanation Generation, Multiple-choice, Multiple Choice Question Answering (MCQA)

Explanation generation introduced as the world tree corpus (Jansen et al., 2018) is an emerging NLP task involving multi-hop inference for explaining the correct answer in multiple-choice QA. It is a challenging task evidenced by low state-of-the-art performances(below 60{\%} in F-score) demonstrated on the task. Of the state-of-the-art approaches, fine-tuned transformer-based (Vaswani et al., 2017) BERT models have shown great promise toward continued system performance improvements compared with approaches relying on surface-level cues alone that demonstrate performance saturation. In this work, we take a novel direction by addressing a particular linguistic characteristic of the data {---} we introduce a novel and lightweight focus feature in the transformer-based model and examine task improvements. Our evaluations reveal a significantly positive impact of this lightweight focus feature achieving the highest scores, second only to a significantly computationally intensive system.
PISA: A measure of Preference In Selection of Arguments to model verb argument recoverability
Authors: Giulia Cappelli, Alessandro Lenci
Date: 2020-12-01 00:00:00
Proceeding: Joint Conference on Lexical and Computational Semantics 2020

Our paper offers a computational model of the semantic recoverability of verb arguments, tested in particular on direct objects and Instruments. Our fully distributional model is intended to improve on older taxonomy-based models, which require a lexicon in addition to the training corpus. We computed the selectional preferences of 99 transitive verbs and 173 Instrument verbs as the mean value of the pairwise cosines between their arguments (a weighted mean between all the arguments, or an unweighted mean with the topmost k arguments). Results show that our model can predict the recoverability of objects and Instruments, providing a similar result to that of taxonomy-based models but at a much cheaper computational cost.
Learning Negation Scope from Syntactic Structure
Authors: Nick McKenna, Mark Steedman
Date: 2020-12-01 00:00:00
Proceeding: Joint Conference on Lexical and Computational Semantics 2020
Tasks: Negation, Word Embeddings

We present a semi-supervised model which learns the semantics of negation purely through analysis of syntactic structure. Linguistic theory posits that the semantics of negation can be understood purely syntactically, though recent research relies on combining a variety of features including part-of-speech tags, word embeddings, and semantic representations to achieve high task performance. Our simplified model returns to syntactic theory and achieves state-of-the-art performance on the task of Negation Scope Detection while demonstrating the tight relationship between the syntax and semantics of negation.