Canada

Entity standardization maps noisy mentions from free-form text to standard entities in a knowledge base. The unique challenge of this task relative to other entity-related tasks is the lack of surrounding context and numerous variations in the surface form of the mentions, especially when it comes to generalization across domains where labeled data is scarce. Previous research mostly focuses on developing models either heavily relying on context, or dedicated solely to a specific domain. In contrast, we pro- pose CoSiNES, a generic and adaptable framework with Contrastive Siamese Network for Entity Standardization that effectively adapts a pretrained language model to capture the syntax and semantics of the entities in a new domain.
We construct a new dataset in the technology domain, which contains 640 technical stack entities and 6,412 mentions collected from industrial content management systems. We demonstrate that CoSiNES yields higher accuracy and faster runtime than baselines derived from leading methods in this domain. CoSiNES also achieves competitive performance in four standard datasets from the chemistry, medicine, and biomedical domains, demonstrating its cross-domain applicability.


ACL 2023

CoSiNES: Contrastive Siamese Network for Entity Standardization

entity standardization

contrastive learning

entity linking

# Welcome everyone to ACL 2022!

The 60th Annual Meeting of the Association for Computational Linguistics is taking place May 22-27, 2022 as a hybrid event, in Dublin and online. We are happy to welcome all of you to this anniversary edition with an almost 50-50 in-person and virtual participation. 
The main conference program features oral presentations, in-person and virtual posters and demo sessions, a plenary session for our best paper presentations and awards, three amazing keynote events and two new initiatives of invited talks: Spotlight Talks for Young Rising Stars (STIRS) and The Next Big Idea Talks. Posters (including Findings of ACL 2022) and demos are grouped by areas for both the in-person and the virtual sessions. For the virtual component, the talks will be on Zoom and the posters and the demos will be in GatherTown. The Student Research Workshop will have an oral session and a poster session as part of Poster Session 1. The program also features eight Tutorials and 28 Workshops. 

 
We wish you a wonderful conference! 
[**The ACL 2022 Organizing Committee**](https://www.2022.aclweb.org/organisers)
 
[**Conference Handbook**](https://drive.google.com/file/d/1_BUCMfhMVrjG9E2e71aHdHeE28KSje0l/view?usp=sharing) 
[**Mini Handbook**](https://drive.google.com/file/d/1qlBKl0wzmlVF1oCeMQl3BahLd9nLP5Ce/view?usp=sharing) 
[**Posters and Demo guides**](https://drive.google.com/file/d/1UucMAoCNncIOaH1rMMDa0owuG9qgvJTG/view?usp=sharing)

ACL 2022

The Association for Computational Linguistics (ACL) is the premier international scientific and professional society for people working on computational problems involving human language, a field often referred to as either computational linguistics or natural language processing (NLP). 

workshop paper

### Welcome to ACL 2023, the 61st Annual Meeting of the Association for Computational Linguistics! 
 The conference will be held in Toronto, Canada, July 9-14, 2023. 
Following the succession of the recent conferences in our field, ACL 2023 will adopt a hybrid format.
While the impact of Covid has considerably diminished in terms of traveling, obtaining visas to Canada
entails a very long process. Moreover, the global economic conditions pose challenges for many individuals to travel to conferences. Recognizing these circumstances, we know many participants may not be
able to attend the conference in person. Therefore, we are committed to providing a great virtual platform
so everyone has the opportunity to interact with other participants and enjoy the conference. Based on the
current registered participants, approxiately 30% have chosen to attend the conference virtually. Whether
you join us in person or virtually, we sincerely hope everyone has a remarkable conference experience. 
This General Chair’s message is where I express my gratitude to the many individuals who have made
enormous contributions to the conference over the past year.

Read [**ACL 2023 General Chair's message**](https://docs.google.com/document/d/1WobYM7norbG4dI48s75HfJoD89qgX5a_F-6U8AteLSA/edit?usp=sharing/) in full.

##### **[Conference Handbook](https://2023.aclweb.org/downloads/acl2023-handbook.pdf)**

The Association for Computational Linguistics (ACL) is the premier international scientific and professional society for people working on computational problems involving human language, a field often referred to as either computational linguistics or natural language processing.

In contrast to large text corpora, knowledge graphs (KG) provide dense and structured representations of factual information. This makes them attractive for systems that supplement or ground the knowledge found in pre-trained language models with an external knowledge source. This has especially been the case for classification tasks, where recent work has focused on creating pipeline models that retrieve information from KGs like ConceptNet as additional context. Many of these models consist of multiple components, and although they differ in the number and nature of these parts, they all have in common that for some given text query, they attempt to identify and retrieve a relevant subgraph from the KG. Due to the noise and idiosyncrasies often found in KGs, it is not known how current methods compare to a scenario where the aligned subgraph is completely relevant to the query. In this work, we try to bridge this knowledge gap by reviewing current approaches to text-to-KG alignment and evaluating them on two datasets where manually created graphs are available, providing insights into the effectiveness of current methods.

Text-To-KG Alignment: Comparing Current Methods on Classification Tasks

Humans often describe complex quantitative data using trend-based patterns. Trend-based patterns can be interpreted as higher order functions and relations over numerical data such as extreme values, rates of change, or cyclical repetition. One application where trends abound are descriptions of numerical tabular data. Therefore, the alignment of numerical tables and textual description of trends enables easier interpretations of tables. 
Most existing approaches can align quantities in text with tabular data but are unable to detect and align trend-based patterns about data. In this paper, we introduce the initial steps for aligning trend-based patterns about the data, i.e. the detection of textual description of trends and the alignment of trends with a relevant table. We introduce the problem of identifying quantifiably verifiable statements (QVS) in the text and aligning them with tables and datasets. We define the structure of these statements and implement a structured based detection. In our experiments, we demonstrate our method can detect and align these statements from several domains and compare favorably with traditional sequence labeling methods.

Identifying Quantifiably Verifiable Statements from Text

Event extraction is a complex task that involves extracting events from unstructured text. Prior classification-based methods require comprehensive entity annotations for joint training, while newer generation-based methods rely on heuristic templates containing oracle information such as event type, which is often unavailable in real-world scenarios. In this study, we consider a more realistic task setting, namely the Oracle-Free Event Extraction (OFEE) task, where only the input context is given, without any oracle information including event type, event ontology, or trigger word. To address this task, we propose a new framework, COFFEE. This framework extracts events solely based on the document context, without referring to any oracle information. In particular, COFFEE introduces a contrastive selection model to refine the generated triggers and handle multi-event instances. Our proposed COFFEE outperforms state-of-the-art approaches in the oracle-free setting of the event extraction task, as evaluated on two public variants of the ACE05 benchmark.

COFFEE: A Contrastive Oracle-Free Framework for Event Extraction

Relation extraction is a crucial language processing task for various downstream applications, including knowledge base completion, question answering, and summarization. Traditional relation-extraction techniques, however, rely on a predefined set of relations and model the extraction as a classification task. Consequently, such closed-world extraction methods are insufficient for inducing novel relations from a corpus. Unsupervised techniques like OpenIE, which extract <head, relation, tail> triples, generate relations that are too general for practical information extraction applications. In this work, we contribute the following: 1) We motivate and introduce a new task, corpus-based task-specific relation discovery. 2) We adapt existing data sources to create Wiki-Art, a novel dataset for task-specific relation discovery. 3) We develop a novel framework for relation discovery using zero-shot entity linking, prompting, and type-specific clustering. Our approach effectively connects unstructured text spans to their shared underlying relations, bridging the data-representation gap and significantly outperforming baselines on both quantitative and qualitative metrics.

Corpus-Based Task-Specific Relation Discovery

Fifteen years of work on entity linking has established the importance of different information sources in making linking decisions: mention and entity name similarity, contextual relevance, and features of the knowledge base. Modern state-of-the-art systems build on these features, including through neural representations (Wu et al. 2020). In contrast to this trend, the autoregressive language model GENRE
(De Cao et al 2021)
generates normalized entity names for mentions and beats many other entity linking systems, despite making no use of knowledge base (KB) information. 
How is this possible? We analyze the behavior of GENRE on several entity linking datasets and demonstrate that its performance stems from memorization of name patterns. In contrast, it fails in cases that might benefit from using the KB. We experiment with a modification to the model to enable it to utilize KB information, highlighting challenges to incorporating traditional entity linking information sources into autoregressive models.

On the Surprising Effectiveness of Name Matching Alone in Autoregressive Entity Linking

Since conventional knowledge embedding models
cannot take full advantage of the abundant
textual information, there have been extensive
research efforts in enhancing knowledge embedding
using texts. However, existing enhancement
approaches cannot apply to temporal
knowledge graphs (tKGs), which contain
time-dependent event knowledge with complex
temporal dynamics. Specifically, existing
enhancement approaches often assume knowledge
embedding is time-independent. In contrast,
the entity embedding in tKG models usually
evolves, which poses the challenge of aligning
temporally relevant texts with entities. To
this end, we propose to study enhancing temporal
knowledge embedding with textual data
in this paper. As an approach to this task, we
propose Enhanced Temporal Knowledge Embeddings
with Contextualized Language Representations
(ECOLA), which takes the temporal
aspect into account and injects textual information
into temporal knowledge embedding.
To evaluate ECOLA, we introduce three new
datasets for training and evaluating ECOLA.
Extensive experiments show that ECOLA significantly
enhances temporal KG embedding
models with up to 287% relative improvements
regarding Hits@1 on the link prediction task.
The code and models are publicly available.

ECOLA: Enhancing Temporal Knowledge Embeddings with Contextualized Language Representations

Information extraction (IE) systems aim to automatically extract structured information, such as named entities, relations between entities, and events, from unstructured texts. While most existing work addresses a particular IE task, universally modeling various IE tasks with one model has achieved great success recently. Despite their success, they employ a one-stage learning strategy, i.e., directly learning to extract the target structure given the input text, which contradicts the human learning process. In this paper, we propose a unified easy-to-hard learning framework consisting of three stages, i.e., the easy stage, the hard stage, and the main stage, for IE by mimicking the human learning process. By breaking down the learning process into multiple stages, our framework facilitates the model to acquire general IE task knowledge and improve its generalization ability. Extensive experiments across four IE tasks demonstrate the effectiveness of our framework. We achieve new state-of-the-art re- results on 13 out of 17 datasets.

Easy-to-Hard Learning for Information Extraction

Relation Extraction (RE) remains a challenging task, especially when considering realistic out-of-domain evaluations. One of the main reasons for this is the limited training size of current RE datasets: obtaining high-quality (manually annotated) data is extremely expensive and cannot realistically be repeated for each new domain. An intermediate training step on data from related tasks has shown to be beneficial across many NLP tasks.However, this setup still requires supplementary annotated data, which is often not available. In this paper, we investigate intermediate pre-training specifically for RE. We exploit the affinity between syntactic structure and semantic RE, and identify the syntactic relations which are closely related to RE by being on the shortest dependency path between two entities. We then take advantage of the high accuracy of current syntactic parsers in order to automatically obtain large amounts of low-cost pre-training data. By pre-training our RE model on the relevant syntactic relations, we are able to outperform the baseline in five out of six cross-domain setups, without any additional annotated data.

Silver Syntax Pre-training for Cross-Domain Relation Extraction

Information Synchronization of semistructured data across languages is challenging. For instance, Wikipedia tables in one language should be synchronized across languages. To address this problem, we introduce a new dataset INFOSYNC and a two-step method for tabular synchronization. INFOSYNC contains 100K entity-centric tables (Wikipedia Infoboxes) across 14 languages, of which a subset (∼3.5K pairs) are manually annotated. The proposed method includes 1) Information Alignment to map rows and 2) Information Update for updating missing/outdated information for aligned tables across multilingual tables. When evaluated on INFOSYNC, information alignment achieves an F1 score of 87.91 (en ↔ non-en). To evaluate information updation, we perform human-assisted Wikipedia edits on Infoboxes for 603 table pairs. Our approach obtains an acceptance rate of 77.28% on Wikipedia, showing the effectiveness of the proposed method.

InfoSync: Information Synchronization across Multilingual Semi-structured Tables

Recent work has shown that fine-tuning large language models (LLMs) on large-scale instruction-following datasets substantially improves their performance on a wide range of NLP tasks, especially in the zero-shot setting. However, even advanced instruction-tuned LLMs still fail to outperform small LMs on relation extraction (RE), a fundamental information extraction task. We hypothesize that instruction-tuning has been unable to elicit strong RE capabilities in LLMs due to RE’s low incidence in instruction-tuning datasets, making up less than 1% of all tasks (Wang et al., 2022). To address this limitation, we propose QA4RE, a framework that aligns RE with question answering (QA), a predominant task in instruction-tuning datasets. Comprehensive zero-shot RE experiments over four datasets with two series of instruction-tuned LLMs (six
LLMs in total) demonstrate that our QA4RE framework consistently improves LLM performance, strongly verifying our hypothesis and enabling LLMs to outperform strong zero-shot baselines by a large margin. Additionally, we provide thorough experiments and discussions to show the robustness, few-shot effectiveness, and strong transferability of our QA4RE framework. This work illustrates a promising way of adapting LLMs to challenging and underrepresented tasks by aligning these tasks with more common instruction-tuning tasks like QA.

Downloads

Next from ACL 2023

Text-To-KG Alignment: Comparing Current Methods on Classification Tasks

Similar lecture

A Study on Entity Linking Across Domains":" Which Data is Best for Fine-Tuning?