pwc-archive/papers-with-abstracts huggingface dataset:
Applied Medical Code Mapping with Character-based Deep Learning Models and Word-based Logic
Authors: John Langton, Krishna Srihasam
Date: 2021-06-01 00:00:00
Proceeding: ACL (NALOMA, IWCS) 2021 6

Logical Observation Identifiers Names and Codes (LOINC) is a standard set of codes that enable clinicians to communicate about medical tests. Laboratories depend on LOINC to identify what tests a doctor orders for a patient. However, clinicians often use site specific, custom codes in their medical records systems that can include shorthand, spelling mistakes, and invented acronyms. Software solutions must map from these custom codes to the LOINC standard to support data interoperability. A key challenge is that LOINC is comprised of six elements. Mapping requires not only extracting those elements, but also combining them according to LOINC logic. We found that character-based deep learning excels at extracting LOINC elements while logic based methods are more effective for combining those elements into complete LOINC values. In this paper, we present an ensemble of machine learning and logic that is currently used in several medical facilities to map from
Transferring Representations of Logical Connectives
Authors: Aaron Traylor, Ellie Pavlick, Roman Feiman
Date: 2021-06-01 00:00:00
Proceeding: ACL (NALOMA, IWCS) 2021 6
Tasks: Language Modeling, Language Modelling

In modern natural language processing pipelines, it is common practice to “pretrain” a generative language model on a large corpus of text, and then to “finetune” the created representations by continuing to train them on a discriminative textual inference task. However, it is not immediately clear whether the logical meaning necessary to model logical entailment is captured by language models in this paradigm. We examine this pretrain-finetune recipe with language models trained on a synthetic propositional language entailment task, and present results on test sets probing models’ knowledge of axioms of first order logic.
Monotonic Inference for Underspecified Episodic Logic
Authors: Gene Kim, Mandar Juvekar, Lenhart Schubert
Date: 2021-06-01 00:00:00
Proceeding: ACL (NALOMA, IWCS) 2021 6

We present a method of making natural logic inferences from Unscoped Logical Form of Episodic Logic. We establish a correspondence between inference rules of scope resolved Episodic Logic and the natural logic treatment by Sánchez Valencia (1991a), and hence demonstrate the ability to handle foundational natural logic inferences from prior literature as well as more general nested monotonicity inferences.
Bayesian Classification and Inference in a Probabilistic Type Theory with Records
Authors: Staffan Larsson, Robin Cooper
Date: 2021-06-01 00:00:00
Proceeding: ACL (NALOMA, IWCS) 2021 6
Tasks: Classification, Vocal Bursts Type Prediction

We propose a probabilistic account of semantic inference and classification formulated in terms of probabilistic type theory with records, building on Cooper et. al. (2014) and Cooper et. al. (2015). We suggest probabilistic type theoretic formulations of Naive Bayes Classifiers and Bayesian Networks. A central element of these constructions is a type-theoretic version of a random variable. We illustrate this account with a simple language game combining probabilistic classification of perceptual input with probabilistic (semantic) inference.
From compositional semantics to Bayesian pragmatics via logical inference
Authors: Julian Grove, Jean-Philippe Bernardy, Stergios Chatzikyriakidis
Date: 2021-06-01 00:00:00
Proceeding: ACL (NALOMA, IWCS) 2021 6
Tasks: World Knowledge

Formal semantics in the Montagovian tradition provides precise meaning characterisations, but usually without a formal theory of the pragmatics of contextual parameters and their sensitivity to background knowledge. Meanwhile, formal pragmatic theories make explicit predictions about meaning in context, but generally without a well-defined compositional semantics. We propose a combined framework for the semantic and pragmatic interpretation of sentences in the face of probabilistic knowledge. We do so by (1) extending a Montagovian interpretation scheme to generate a distribution over possible meanings, and (2) generating a posterior for this distribution using a variant of the Rational Speech Act (RSA) models, but generalised to arbitrary propositions. These aspects of our framework are tied together by evaluating entailment under probabilistic uncertainty. We apply our model to anaphora resolution and show that it provides expected biases under suitable assumptions about the distributions of lexical and world-knowledge. Further, we observe that the model’s output is robust to variations in its parameters within reasonable ranges.
A (Mostly) Symbolic System for Monotonic Inference with Unscoped Episodic Logical Forms
Authors: Gene Kim, Mandar Juvekar, Junis Ekmekciu, Viet Duong, Lenhart Schubert
Date: 2021-06-01 00:00:00
Proceeding: ACL (NALOMA, IWCS) 2021 6

We implement the formalization of natural logic-like monotonic inference using Unscoped Episodic Logical Forms (ULFs) by Kim et al. (2020). We demonstrate this system’s capacity to handle a variety of challenging semantic phenomena using the FraCaS dataset (Cooper et al., 1996). These results give empirical evidence for prior claims that ULF is an appropriate representation to mediate natural logic-like inferences.
Applied Temporal Analysis: A Complete Run of the FraCaS Test Suite
Authors: Jean-Philippe Bernardy, Stergios Chatzikyriakidis
Date: 2021-06-01 00:00:00
Proceeding: IWCS (ACL) 2021 6

In this paper, we propose an implementation of temporal semantics that translates syntax trees to logical formulas, suitable for consumption by the Coq proof assistant. The analysis supports a wide range of phenomena including: temporal references, temporal adverbs, aspectual classes and progressives. The new semantics are built on top of a previous system handling all sections of the FraCaS test suite except the temporal reference section, and we obtain an accuracy of 81 percent overall and 73 percent for the problems explicitly marked as related to temporal reference. To the best of our knowledge, this is the best performance of a logical system on the whole of the FraCaS.
Computing All Quantifier Scopes with CCG
Authors: Miloš Stanojević, Mark Steedman
Date: 2021-06-01 00:00:00
Proceeding: IWCS (ACL) 2021 6
Tasks: All

We present a method for computing all quantifer scopes that can be extracted from a single CCG derivation. To do that we build on the proposal of Steedman (1999, 2011) where all existential quantifiers are treated as Skolem functions. We extend the approach by introducing a better packed representation of all possible specifications that also includes node addresses where the specifications happen. These addresses are necessary for recovering all, and only, possible readings.
Predicate Representations and Polysemy in VerbNet Semantic Parsing
Authors: James Gung, Martha Palmer
Date: 2021-06-01 00:00:00
Proceeding: IWCS (ACL) 2021 6
Tasks: Semantic Parsing, Semantic Role Labeling

Despite recent advances in semantic role labeling propelled by pre-trained text encoders like BERT, performance lags behind when applied to predicates observed infrequently during training or to sentences in new domains. In this work, we investigate how role labeling performance on low-frequency predicates and out-of-domain data can be further improved by using VerbNet, a verb lexicon that groups verbs into hierarchical classes based on shared syntactic and semantic behavior and defines semantic representations describing relations between arguments. We find that VerbNet classes provide an effective level of abstraction, improving generalization on low-frequency predicates by allowing them to learn from the training examples of other predicates belonging to the same class. We also find that joint training of VerbNet role labeling and predicate disambiguation of VerbNet classes for polysemous verbs leads to improvements in both tasks, naturally supporting the extraction of VerbNet’s semantic representations.
Looking for a Role for Word Embeddings in Eye-Tracking Features Prediction: Does Semantic Similarity Help?
Authors: Lavinia Salicchi, Alessandro Lenci, Emmanuele Chersoni
Date: 2021-06-01 00:00:00
Proceeding: IWCS (ACL) 2021 6
Tasks: Semantic Similarity, Semantic Textual Similarity, Word Embeddings

Eye-tracking psycholinguistic studies have suggested that context-word semantic coherence and predictability influence language processing during the reading activity. In this study, we investigate the correlation between the cosine similarities computed with word embedding models (both static and contextualized) and eye-tracking data from two naturalistic reading corpora. We also studied the correlations of surprisal scores computed with three state-of-the-art language models. Our results show strong correlation for the scores computed with BERT and GloVe, suggesting that similarity can play an important role in modeling reading times.
Automatic Assignment of Semantic Frames in Disaster Response Team Communication Dialogues
Authors: Natalia Skachkova, Ivana Kruijff-Korbayova
Date: 2021-06-01 00:00:00
Proceeding: IWCS (ACL) 2021 6
Tasks: Disaster Response

We investigate frame semantics as a meaning representation framework for team communication in a disaster response scenario. We focus on the automatic frame assignment and retrain PAFIBERT, which is one of the state-of-the-art frame classifiers, on English and German disaster response team communication data, obtaining accuracy around 90%. We examine the performance of both models and discuss their adjustments, such as sampling of additional training instances from an unrelated domain and adding extra lexical and discourse features to input token representations. We show that sampling has some positive effect on the German frame classifier, discuss an unexpected impact of extra features on the models’ behaviour and perform a careful error analysis.
Implicit representations of event properties within contextual language models: Searching for “causativity neurons”
Authors: Esther Seyffarth, Younes Samih, Laura Kallmeyer, Hassan Sajjad
Date: 2021-06-01 00:00:00
Proceeding: IWCS (ACL) 2021 6
Tasks: Sentence

This paper addresses the question to which extent neural contextual language models such as BERT implicitly represent complex semantic properties. More concretely, the paper shows that the neuron activations obtained from processing an English sentence provide discriminative features for predicting the (non-)causativity of the event denoted by the verb in a simple linear classifier. A layer-wise analysis reveals that the relevant properties are mostly learned in the higher layers. Moreover, further experiments show that appr. 10% of the neuron activations are enough to already predict causativity with a relatively high accuracy.
Is that really a question? Going beyond factoid questions in NLP
Authors: Aikaterini-Lida Kalouli, Rebecca Kehlbeck, Rita Sevastjanova, Oliver Deussen, Daniel Keim, Miriam Butt
Date: 2021-06-01 00:00:00
Proceeding: IWCS (ACL) 2021 6

Research in NLP has mainly focused on factoid questions, with the goal of finding quick and reliable ways of matching a query to an answer. However, human discourse involves more than that: it contains non-canonical questions deployed to achieve specific communicative goals. In this paper, we investigate this under-studied aspect of NLP by introducing a targeted task, creating an appropriate corpus for the task and providing baseline models of diverse nature. With this, we are also able to generate useful insights on the task and open the way for future research in this direction.
New Domain, Major Effort? How Much Data is Necessary to Adapt a Temporal Tagger to the Voice Assistant Domain
Authors: Touhidul Alam, Alessandra Zarcone, Sebastian Padó
Date: 2021-06-01 00:00:00
Proceeding: IWCS (ACL) 2021 6
Tasks: Transfer Learning

Reliable tagging of Temporal Expressions (TEs, e.g., Book a table at L’Osteria for Sunday evening) is a central requirement for Voice Assistants (VAs). However, there is a dearth of resources and systems for the VA domain, since publicly-available temporal taggers are trained only on substantially different domains, such as news and clinical text. Since the cost of annotating large datasets is prohibitive, we investigate the trade-off between in-domain data and performance in DA-Time, a hybrid temporal tagger for the English VA domain which combines a neural architecture for robust TE recognition, with a parser-based TE normalizer. We find that transfer learning goes a long way even with as little as 25 in-domain sentences: DA-Time performs at the state of the art on the news domain, and substantially outperforms it on the VA domain.
Breeding Fillmore’s Chickens and Hatching the Eggs: Recombining Frames and Roles in Frame-Semantic Parsing
Authors: Gosse Minnema, Malvina Nissim
Date: 2021-06-01 00:00:00
Proceeding: IWCS (ACL) 2021 6
Tasks: Semantic Parsing

Frame-semantic parsers traditionally predict predicates, frames, and semantic roles in a fixed order. This paper explores the ‘chicken-or-egg’ problem of interdependencies between these components theoretically and practically. We introduce a flexible BERT-based sequence labeling architecture that allows for predicting frames and roles independently from each other or combining them in several ways. Our results show that our setups can approximate more complex traditional models’ performance, while allowing for a clearer view of the interdependencies between the pipeline’s components, and of how frame and role prediction models make different use of BERT’s layers.
Large-scale text pre-training helps with dialogue act recognition, but not without fine-tuning
Authors: Bill Noble, Vladislav Maraev
Date: 2021-06-01 00:00:00
Proceeding: IWCS (ACL) 2021 6

We use dialogue act recognition (DAR) to investigate how well BERT represents utterances in dialogue, and how fine-tuning and large-scale pre-training contribute to its performance. We find that while both the standard BERT pre-training and pretraining on dialogue-like data are useful, task-specific fine-tuning is essential for good performance.
Builder, we have done it: Evaluating & Extending Dialogue-AMR NLU Pipeline for Two Collaborative Domains
Authors: Claire Bonial, Mitchell Abrams, David Traum, Clare Voss
Date: 2021-06-01 00:00:00
Proceeding: IWCS (ACL) 2021 6
Tasks: Abstract Meaning Representation, AMR Parsing, Natural Language Understanding

We adopt, evaluate, and improve upon a two-step natural language understanding (NLU) pipeline that incrementally tames the variation of unconstrained natural language input and maps to executable robot behaviors. The pipeline first leverages Abstract Meaning Representation (AMR) parsing to capture the propositional content of the utterance, and second converts this into “Dialogue-AMR,” which augments standard AMR with information on tense, aspect, and speech acts. Several alternative approaches and training datasets are evaluated for both steps and corresponding components of the pipeline, some of which outperform the original. We extend the Dialogue-AMR annotation schema to cover a different collaborative instruction domain and evaluate on both domains. With very little training data, we achieve promising performance in the new domain, demonstrating the scalability of this approach.
“Politeness, you simpleton!” retorted [MASK]: Masked prediction of literary characters
Authors: Eric Holgate, Katrin Erk
Date: 2021-06-01 00:00:00
Proceeding: IWCS (ACL) 2021 6
Tasks: Entity Embeddings, Sentence

What is the best way to learn embeddings for entities, and what can be learned from them? We consider this question for the case of literary characters. We address the highly challenging task of guessing, from a sentence in the novel, which character is being talked about, and we probe the embeddings to see what information they encode about their literary characters. We find that when continuously trained, entity embeddings do well at the masked entity prediction task, and that they encode considerable information about the traits and characteristics of the entities.
Tuning Deep Active Learning for Semantic Role Labeling
Authors: Skatje Myers, Martha Palmer
Date: 2021-06-01 00:00:00
Proceeding: IWCS (ACL) 2021 6
Tasks: Active Learning, Semantic Role Labeling, Sentence

Active learning has been shown to reduce annotation requirements for numerous natural language processing tasks, including semantic role labeling (SRL). SRL involves labeling argument spans for potentially multiple predicates in a sentence, which makes it challenging to aggregate the numerous decisions into a single score for determining new instances to annotate. In this paper, we apply two ways of aggregating scores across multiple predicates in order to choose query sentences with two methods of estimating model certainty: using the neural network’s outputs and using dropout-based Bayesian Active Learning by Disagreement. We compare these methods with three passive baselines — random sentence selection, random whole-document selection, and selecting sentences with the most predicates — and analyse the effect these strategies have on the learning curve with respect to reducing the number of annotated sentences and predicates to achieve high performance.
SemLink 2.0: Chasing Lexical Resources
Authors: Kevin Stowe, Jenette Preciado, Kathryn Conger, Susan Windisch Brown, Ghazaleh Kazeminejad, James Gung, Martha Palmer
Date: 2021-06-01 00:00:00
Proceeding: IWCS (ACL) 2021 6

The SemLink resource provides mappings between a variety of lexical semantic ontologies, each with their strengths and weaknesses. To take advantage of these differences, the ability to move between resources is essential. This work describes advances made to improve the usability of the SemLink resource: the automatic addition of new instances and mappings, manual corrections, sense-based vectors and collocation information, and architecture built to automatically update the resource when versions of the underlying resources change. These updates improve coverage, provide new tools to leverage the capabilities of these resources, and facilitate seamless updates, ensuring the consistency and applicability of these mappings in the future.
Variation in framing as a function of temporal reporting distance
Authors: Levi Remijnse, Marten Postma, Piek Vossen
Date: 2021-06-01 00:00:00
Proceeding: IWCS (ACL) 2021 6
Tasks: Diagnostic

In this paper, we measure variation in framing as a function of foregrounding and backgrounding in a co-referential corpus with a range of temporal distance. In one type of experiment, frame-annotated corpora grouped under event types were contrasted, resulting in a ranking of frames with typicality rates. In contrasting between publication dates, a different ranking of frames emerged for documents that are close to or far from the event instance. In the second type of analysis, we trained a diagnostic classifier with frame occurrences in order to let it differentiate documents based on their temporal distance class (close to or far from the event instance). The classifier performs above chance and outperforms models with words.
Automatic Classification of Attributes in German Adjective-Noun Phrases
Authors: Neele Falk, Yana Strakatova, Eva Huber, Erhard Hinrichs
Date: 2021-06-01 00:00:00
Proceeding: IWCS (ACL) 2021 6
Tasks: Attribute, Classification, Data Augmentation, Memorization

Adjectives such as heavy (as in heavy rain) and windy (as in windy day) provide possible values for the attributes intensity and climate, respectively. The attributes themselves are not overtly realized and are in this sense implicit. While these attributes can be easily inferred by humans, their automatic classification poses a challenging task for computational models. We present the following contributions: (1) We gain new insights into the attribute selection task for German. More specifically, we develop computational models for this task that are able to generalize to unseen data. Moreover, we show that classification accuracy depends, inter alia, on the degree of polysemy of the lexemes involved, on the generalization potential of the training data and on the degree of semantic transparency of the adjective-noun pairs in question. (2) We provide the first resource for computational and linguistic experiments with German adjective-noun pairs that can be used for attribute selection and related tasks. In order to safeguard against unwelcome memorization effects, we present an automatic data augmentation method based on a lexical resource that can increase the size of the training data to a large extent.
Are Gestures Worth a Thousand Words? An Analysis of Interviews in the Political Domain
Authors: Daniela Trotta, Sara Tonelli
Date: 2021-06-01 00:00:00
Proceeding: ACL (mmsr, IWCS) 2021 6
Tasks: Retrieval

Speaker gestures are semantically co-expressive with speech and serve different pragmatic functions to accompany oral modality. Therefore, gestures are an inseparable part of the language system: they may add clarity to discourse, can be employed to facilitate lexical retrieval and retain a turn in conversations, assist in verbalizing semantic content and facilitate speakers in coming up with the words they intend to say. This aspect is particularly relevant in political discourse, where speakers try to apply communication strategies that are both clear and persuasive using verbal and non-verbal cues. In this paper we investigate the co-speech gestures of several Italian politicians during face-to-face interviews using a multimodal linguistic approach. We first enrich an existing corpus with a novel annotation layer capturing the function of hand movements. Then, we perform an analysis of the corpus, focusing in particular on the relationship between hand movements and other information layers such as the political party or non-lexical and semi-lexical tags. We observe that the recorded differences pertain more to single politicians than to the party they belong to, and that hand movements tend to occur frequently with semi-lexical phenomena, supporting the lexical retrieval hypothesis.
Requesting clarifications with speech and gestures
Authors: Jonathan Ginzburg, Andy Luecking
Date: 2021-06-01 00:00:00
Proceeding: ACL (mmsr, IWCS) 2021 6

In multimodal natural language interaction both speech and non-speech gestures are involved in the basic mechanism of grounding and repair. We discuss a couple of multimodal clarifica- tion requests and argue that gestures, as well as speech expressions, underlie comparable paral- lelism constraints. In order to make this precise, we slightly extend the formal dialogue frame- work KoS to cover also gestural counterparts of verbal locutionary propositions.
How Vision Affects Language: Comparing Masked Self-Attention in Uni-Modal and Multi-Modal Transformer
Authors: Nikolai Ilinykh, Simon Dobnik
Date: 2021-06-01 00:00:00
Proceeding: ACL (mmsr, IWCS) 2021 6
Tasks: Image Captioning, Machine Translation, Text Generation, Translation

The problem of interpretation of knowledge learned by multi-head self-attention in transformers has been one of the central questions in NLP. However, a lot of work mainly focused on models trained for uni-modal tasks, e.g. machine translation. In this paper, we examine masked self-attention in a multi-modal transformer trained for the task of image captioning. In particular, we test whether the multi-modality of the task objective affects the learned attention patterns. Our visualisations of masked self-attention demonstrate that (i) it can learn general linguistic knowledge of the textual input, and (ii) its attention patterns incorporate artefacts from visual modality even though it has never accessed it directly. We compare our transformer’s attention patterns with masked attention in distilgpt-2 tested for uni-modal text generation of image captions. Based on the maps of extracted attention weights, we argue that masked self-attention in image captioning transformer seems to be enhanced with semantic knowledge from images, exemplifying joint language-and-vision information in its attention patterns.