United Arab Emirates

In natural language understanding (NLU) production systems, users&#39; evolving needs necessitate the addition of new features over time, indexed by new symbols added to the meaning representation space. This requires additional training data and results in ever-growing datasets. We present the first systematic investigation into this incremental symbol learning scenario. Our analysis reveals a troubling quirk in building broad-coverage NLU systems: as the training dataset grows, performance on a small set of new symbols often decreases. We show that this trend holds for multiple mainstream models on two common NLU tasks: intent recognition and semantic parsing. Rejecting class imbalance as the sole culprit, we reveal that the trend is closely associated with an effect we call source signal dilution, where strong lexical cues for the new symbol become diluted as the training dataset grows. Selectively dropping training examples to prevent dilution often reverses the trend, showing the over-reliance of mainstream neural NLU models on simple lexical cues.

EMNLP 2022

When More Data Hurts: A Troubling Quirk in Developing Broad-Coverage Natural Language Understanding Systems

semantic parsing

task-oriented dialogue

natural language understanding

In natural language understanding (NLU) production systems, users' evolving needs necessitate the addition of new features over time, indexed by new symbols added to the meaning representation space. This requires additional training data and results in ever-growing datasets. We present the first systematic investigation into this incremental symbol learning scenario. Our analysis reveals a troubling quirk in building broad-coverage NLU systems: as the training dataset grows, performance on a small set of new symbols often decreases. We show that this trend holds for multiple mainstream models on two common NLU tasks: intent recognition and semantic parsing. Rejecting class imbalance as the sole culprit, we reveal that the trend is closely associated with an effect we call source signal dilution, where strong lexical cues for the new symbol become diluted as the training dataset grows. Selectively dropping training examples to prevent dilution often reverses the trend, showing the over-reliance of mainstream neural NLU models on simple lexical cues.

poster

## Welcome to EMNLP 2022!
I am delighted to welcome you to EMNLP 2022! I believe this conference has been complicated beyond any precedent. Over the past year, it’s been thrilling to see the organization team approach each new puzzle with creativity and enthusiasm. We hope that those participating in Abu Dhabi as well as those joining remotely will leave the conference feeling newly inspired by the program and newly connected to our ever-growing community. Following EMNLP 2021 and major NLP conferences since, EMNLP 2022 is “hybrid,” serving both virtual and in-person participants.

Our key innovations for EMNLP 2022 include:

* EMNLP 2022 is “hybrid” in a second sense, as well: we allowed both direct and rolling review paper submissions, building on the pilot experiment of EMNLP 2021, which considered a small number of ARR submissions. 
* Familiar from NAACL but new to EMNLP, we’ve added an industry track.
* During the conference, “portals” will link virtual poster sessions to in-person conference participants during poster sessions each day.
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***EMNLP 2022 General Chair***
 
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EMNLP 2022 will take place in Abu Dhabi from December 7th to December 11th, 2022. And it will be held in hybrid mode, both online and offline.

While there has been substantial progress in text comprehension through simple factoid question answering, more holistic comprehension of a discourse still presents a major challenge (Dunietz et al., 2020). Someone critically reflecting on a text as they read it will pose curiosity-driven, often open-ended questions, which reflect deep understanding of the content and require complex reasoning to answer (Ko et al., 2020; Westera et al., 2020). A key challenge in building and evaluating models for this type of discourse comprehension is the lack of annotated data, especially since collecting answers to such questions requires high cognitive load for annotators.

This paper presents a novel paradigm that enables scalable data collection targeting the comprehension of news documents, viewing these questions through the lens of discourse. The resulting corpus, DCQA (Discourse Comprehension by Question Answering), captures both discourse and semantic links between sentences in the form of free-form, open-ended questions. On an evaluation set that we annotated on questions from Ko et al. (2020), we show that DCQA provides valuable supervision for answering open-ended questions. We additionally design pre-training methods utilizing existing question-answering resources, and use synthetic data to accommodate unanswerable questions.

Discourse Comprehension: A Question Answering Framework to Represent Sentence Connections

Semantic Overlap Summarization (SOS) is a novel and relatively under-explored seq-to-seq task which entails summarizing common information from multiple alternate narratives. One of the major challenges for solving this task is the lack of existing datasets for supervised training. To address this challenge, we propose a novel data augmentation technique, which allows us to create large amount of synthetic data for training a seq-to-seq model that can perform the SOS task. Through extensive experiments using narratives from the news domain, we show that the models fine-tuned using the synthetic dataset provide significant performance improvements over the pre-trained vanilla summarization techniques and are close to the models fine-tuned on the golden training data; which essentially demonstrates the effectiveness of out proposed data augmentation technique for training seq-to-seq models on the SOS task.

Learning to Generate Overlap Summaries through Noisy Synthetic Data

An important task for designing QA systems is answer sentence selection (AS2): selecting the sentence containing (or constituting) the answer to a question from a set of retrieved relevant documents. In this paper, we propose three novel sentence-level transformer pre-training objectives that incorporate paragraph-level semantics within and across documents, to improve the performance of transformers for AS2, and mitigate the requirement of large labeled datasets. Specifically, the model is tasked to predict whether: (i) two sentences are extracted from the same paragraph, (ii) a given sentence is extracted from a given paragraph, and (iii) two paragraphs are extracted from the same document. Our experiments on three public and one industrial AS2 datasets demonstrate the empirical superiority of our pre-trained transformers over baseline models such as RoBERTa and ELECTRA for AS2.

Pre-training Transformer Models with Sentence-Level Objectives for Answer Sentence Selection

We suggest a model for partial diacritization of deep orthographies. We focus on Arabic, where the optional indication of selected vowels by means of diacritics can resolve ambiguity and improve readability. Our partial diacritizer restores short vowels only when they contribute to the ease of understandability during reading a given running text. The idea is to identify those uncertainties of absent vowels that require the reader to look ahead to disambiguate. To achieve this, two independent neural networks are employed for predicting diacritics, one that takes the entire sentence as input and another that considers only the text that has been read thus far. Partial diacritization is then determined by retaining precisely those vowels on which the two networks disagree, preferring the reading based on consideration of the whole sentence over the more naive reading-order diacritization. For evaluation, we prepared a new dataset of Arabic texts with both full and partial vowelization. In addition to facilitating readability, we find that our partial diacritizer improves translation quality compared either to their total absence or to random selection. Lastly, we study the benefit of knowing the text that follows the word in focus towards the restoration of short vowels during reading, and we measure the degree to which lookahead contributes to resolving ambiguities encountered while reading.

How Much Does Lookahead Matter for Disambiguation? Partial Arabic Diacritization Case Study

Peer review is a key component of the publishing process in most fields of science. Increasing submission rates put a strain on reviewing quality and efficiency, motivating the development of applications to support the reviewing and editorial work. While existing NLP studies focus on the analysis of individual texts, editorial assistance often requires modeling interactions between pairs of texts—yet general frameworks and datasets to support this scenario are missing. Relationships between texts are the core object of the intertextuality theory—a family of approaches in literary studies not yet operationalized in NLP. Inspired by prior theoretical work, we propose the first intertextual model of text-based collaboration, which encompasses three major phenomena that make up a full iteration of the review–revise–and–resubmit cycle: pragmatic tagging, linking, and long-document version alignment. While peer review is used across the fields of science and publication formats, existing datasets solely focus on conference-style review in computer science. Addressing this, we instantiate our proposed model in the first annotated multidomain corpus in journal-style post-publication open peer review, and provide detailed insights into the practical aspects of intertextual annotation. Our resource is a major step toward multidomain, fine-grained applications of NLP in editorial support for peer review, and our intertextual framework paves the path for general-purpose modeling of text-based collaboration. We make our corpus, detailed annotation guidelines, and accompanying code publicly available.

Revise and Resubmit: An Intertextual Model of Text-based Collaboration in Peer Review

We present Elevant, a tool for the fully automatic fine-grained evaluation of a set of entity linkers on a set of benchmarks. Elevant provides an automatic breakdown of the performance by various error categories and by entity type. Elevant also provides a rich and compact, yet very intuitive and self-explanatory visualization of the results of a linker on a benchmark in comparison to the ground truth. A live demo, the link to the complete code base on GitHub and a link to a demo video are provided under https://elevant.cs.uni-freiburg.de .

ELEVANT: A Fully Automatic Fine-Grained Entity Linking Evaluation and Analysis Tool

Evaluation is a key part of machine learning (ML), yet there is a lack of support and tooling to enable its informed and systematic practice. We introduce Evaluate and Evaluation on the Hub—a set of tools to facilitate the evaluation of models and datasets in ML. Evaluate is a library to support best practices for measurements, metrics, and comparisons of data and models. Its goal is to support reproducibility of evaluation, centralize and document the evaluation process, and broaden evaluation to cover more facets of model performance. It includes over 50 efficient canonical implementations for a variety of domains and scenarios, interactive documentation, and the ability to easily share implementations and outcomes. The library is available at https://github.com/huggingface/evaluate. In addition, we introduce Evaluation on the Hub, a platform that enables the large-scale evaluation of over 75,000 models and 11,000 datasets on the Hugging Face Hub, for free, at the click of a button. Evaluation on the Hub is available at https://huggingface.co/autoevaluate

Evaluate & Evaluation on the Hub: Better Best Practices for Data and Model Measurements

Knowledge Graphs (KGs) store information in the form of (head, predicate, tail)-triples. To augment KGs with new knowledge, researchers proposed models for KG Completion (KGC) tasks such as link prediction; i.e., answering (h; p; ?) or (?; p; t) queries. Such models are usually evaluated with averaged metrics on a held-out test set. While useful for tracking progress, averaged single-score metrics cannot reveal what exactly a model has learned—or failed to learn. To address this issue, we propose KGxBoard: an interactive framework for performing fine-grained evaluation on meaningful subsets of the data, each of which tests individual and interpretable capabilities of a KGC model. In our experiments, we highlight the findings that we discovered with the use of KGxBoard, which would have been impossible to detect with standard averaged single-score metrics.


KGxBoard: Explainable and Interactive Leaderboard for Evaluation of Knowledge Graph Completion Models

With the advent of Transformers, large language models (LLMs) have saturated well-known NLP benchmarks and leaderboards with high aggregate performance. However, many times these models systematically fail on tail-data or rare groups not obvious in aggregate evaluation. Identifying such problematic data groups is even more challenging when there are no explicit labels (e.g., ethnicity, gender,
etc.) and further compounded for NLP datasets due to the lack of visual features to characterize failure modes (e.g., Asian males, animals indoors, waterbirds on land, etc.). This paper introduces an interactive Systematic Error Analysis
and Labeling (SEAL) tool that uses a two-step approach to first identify high-error slices of data and then, in the second step, introduce methods to give human-understandable semantics to those under-performing slices. We explore a variety of methods for coming up with coherent semantics for the error groups using
language models for semantic labeling and a text-to-image model for generating visual features. SEAL toolkit and demo screencast is available at https://huggingface.co/spaces/ nazneen/seal.

SEAL: Interactive Tool for Systematic Error Analysis and Labeling

Text classification can be useful in many real-world scenarios, saving a lot of time for end users. However, building a classifier generally requires coding skills and ML knowledge, which poses a significant barrier for many potential users. 

To lift this barrier we introduce Label Sleuth, a free open source system for labeling and creating text classifiers. This system is unique for; (a) being a no-code system, making NLP accessible for non-experts, (b) guiding its users throughout the entire labeling process until they obtain their desired classifier, making the process efficient - from cold start to a classifier in a few hours, (c) being open for configuration and extension by developers.

By open sourcing Label Sleuth we hope to build a community of users and developers that will widen the utilization of NLP models.

Website: https://www.label-sleuth.org/
GitHub: https://github.com/label-sleuth/label-sleuth

When More Data Hurts: A Troubling Quirk in Developing Broad-Coverage Natural Language Understanding Systems

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Discourse Comprehension: A Question Answering Framework to Represent Sentence Connections

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