Croatia

One of the major problems with text simplification is the lack of high-quality data. The sources of simplification datasets are limited to Wikipedia and Newsela, restricting further development of this field. In this paper, we analyzed the similarity between text summarization and text simplification and exploited summarization data to help simplify. First, we proposed an alignment algorithm to extract sentence pairs from summarization datasets. Then, we designed four attributes to characterize the degree of simplification and proposed a method to filter suitable pairs. We named these pairs Sum4Simp (S4S). Next, we conducted human evaluations to show that S4S is high-quality and compared it with a real simplification dataset. Finally, we conducted experiments to illustrate that the S4S can improve the performance of several mainstream simplification models, especially in low-resource scenarios.

EACL 2023

Exploiting Summarization Data to Help Text Simplification

a new dataset

text simplification

data augmentation

text generation

poster

### Welcome to the 17th Conference of the European Chapter of the Association for Computational Linguistics (EACL) 
Continuing its mission of expanding and involving the science community of all European countries, EACL had selected the Ukrainian community for the 16th EACL, which took place online due to the COVID pandemic. Unfortunately, the ongoing war made the organisation in Kyiv impossible. Considering the importance of physical interaction among researchers, especially after the restrictions imposed by the COVID pandemic, in addition to an online mode, the conference will be held in Dubrovnik, Croatia, from 2 to 6 of May, 2023. The original aim of strengthening the connection with the Ukrainian community will not change as our program will feature a dedicated session and a workshop to highlight work on Ukrainian language technologies. As the flagship European conference in the field of computational linguistics, EACL welcomes European and international researchers covering a broad spectrum of research areas that are concerned with computational approaches to natural language.

You need to register for the conference in order to access this site. Please visit https://2023.eacl.org/registration for more information.

As the flagship European conference in the field of computational linguistics, EACL welcomes European and international researchers covering a broad spectrum of research areas that are concerned with computational approaches to natural language.

Sentiment analysis has become a central tool in various disciplines outside of natural language processing. In particular in applied and domain-specific settings with strong requirements for interpretable methods, dictionary-based approaches are still a popular choice. However, existing dictionaries are often limited in coverage, static once annotation is completed and sentiment scales differ widely; some are discrete others continuous. We propose a Bayesian generative model that learns a composite sentiment dictionary as an interpolation between six existing dictionaries with different scales. We argue that sentiment is a latent concept with intrinsically ranking-based characteristics—the word "excellent" may be ranked more positive than "great" and "okay", but it is hard to express how much more exactly. This prompts us to enforce an ordinal scale of ordered discrete sentiment values in our dictionary. We achieve this through an ordering transformation in the priors of our model. We evaluate the model intrinsically by imputing missing values in existing dictionaries. Moreover, we conduct extrinsic evaluations through sentiment classification tasks. Finally, we present two extension: first, we present a method to augment dictionary-based approaches with word embeddings to construct sentiment scales along new semantic axes. Second, we demonstrate a Latent Dirichlet Allocation-inspired variant of our model that learns document topics that are ordered by sentiment.

Sentiment as an Ordinal Latent Variable

Many automatic speech recognition (ASR) data sets include a single pre-defined test set consisting of one or more speakers whose speech never appears in the training set. This ``hold-speaker(s)-out'' data partitioning strategy, however, may not be ideal for data sets in which the number of speakers is very small. This study investigates ten different data split methods for five languages with minimal ASR training resources. We find that (1) model performance varies greatly depending on which speaker is selected for testing; (2) the average word error rate (WER) across all held out speakers is comparable not only to the average WER over multiple random splits but also to any given individual random split; (3) WER is also generally comparable when the data is split heuristically or adversarially; (4) utterance duration and intensity are comparatively more predictive factors of variability regardless of the data split. These results suggest that the widely used hold-speakers-out approach to ASR data partitioning can yield results that do not reflect model performance on unseen data or speakers. Random splits can yield more reliable and generalizable estimates when facing data sparsity.

Investigating data partitioning strategies for crosslinguistic low-resource ASR evaluation

Triple-Hybrid Energy-based Model Makes Better Calibrated Natural Language Understanding Models

Open knowledge graph (KG) consists of (subject, relation, object) triples extracted from millions of raw text. The subject and object noun phrases and the relation in open KG have severe redundancy and ambiguity and need to be canonicalized. Existing datasets for open KG canonicalization only provide gold entity-level canonicalization for noun phrases. In this paper, we present COMBO, a Complete Benchmark for Open KG canonicalization. Compared with existing datasets, we additionally provide gold canonicalization for relation phrases, gold ontology-level canonicalization for noun phrases, as well as source sentences from which triples are extracted. We also propose metrics for evaluating each type of canonicalization. On the COMBO dataset, we empirically compare previously proposed canonicalization methods as well as a few simple baseline methods based on pretrained language models. We find that properly encoding the phrases in a triple using pretrained language models results in better relation canonicalization and ontology-level canonicalization of the noun phrase. We release our dataset, baselines, and evaluation scripts at https://github.com/jeffchy/COMBO/tree/main.

COMBO: A Complete Benchmark for Open KG Canonicalization

Human-in-the-loop topic modelling incorporates users' knowledge into the modelling process, enabling them to refine the model iteratively. Recent research has demonstrated the value of user feedback, but there are still issues to consider, such as the difficulty in tracking changes, comparing different models and the lack of evaluation based on real-world examples of use. We developed a novel, interactive human-in-the-loop topic modelling system with a user-friendly interface that enables users to compare and record every step they take, and a novel topic words suggestion feature to help users provide feedback that is faithful to the ground truth. Our system also supports not only what traditional topic models can do, i.e., learning the topics from the whole corpus, but also targeted topic modelling, i.e., learning topics for specific aspects of the corpus. In this article, we provide an overview of the system and present the results of a series of user studies designed to assess the value of the system in progressively more realistic applications of topic modelling.

A User-Centered, Interactive, Human-in-the-Loop Topic Modelling System

This presentation introduces LoFT model, which applies logical forms as fact checkers and content planners to improve faithfulness and diversity of logical table-to-text generation simultaneously.

LoFT: Enhancing Faithfulness and Diversity for Table-to-Text Generation via Logic Form Control

Multi-task learning (MTL) has become increasingly popular in natural language processing (NLP) because it improves the performance of related tasks by exploiting their commonalities and differences. Nevertheless, it is still not understood very well how multi-task learning can be implemented based on the relatedness of training tasks. In this survey, we review recent advances of multi-task learning methods in NLP, with the aim of summarizing them into two general multi-task training methods based on their task relatedness: (i) joint training and (ii) multi-step training. We present examples in various NLP downstream applications, summarize the task relationships and discuss future directions of this promising topic.

A Survey of Multi-task Learning in Natural Language Processing: Regarding Task Relatedness and Training Methods

Aspect-based sentiment analysis (ABSA) has attracted broad attention due to its commercial value. Natural Language Generation-based (NLG) approaches dominate the recent advance in ABSA tasks. However, current NLG practices are inefficient because most of them directly employ an autoregressive generation framework that cannot efficiently generate location information and semantic representations of ABSA targets. In this paper, we propose a novel framework, namely Efficient Hybrid Generation (EHG) to revolutionize traditions. Specifically, we leverage an Efficient Hybrid Transformer to generate the location and semantic information of ABSA targets in parallel. Besides, we design a novel global hybrid loss function in combination with bipartite matching to achieve end-to-end model training. 
Extensive experiments demonstrate that our proposed EHG framework greatly improves the efficiency of NLG-based methods and outperforms the competitive baselines in almost all cases.

Efficient Hybrid Generation Framework for Aspect-Based Sentiment Analysis

Semantic parsing is a technique aimed at constructing a structured representation of the meaning of a natural-language question. Recent advances in language models trained on code have shown superior performance in generating these representations compared to language models trained solely on natural language text. The existing fine-tuned neural semantic parsers are vulnerable to adversarial attacks on natural-language inputs. While it has been established that the robustness of smaller semantic parsers can be enhanced through adversarial training, this approach is not feasible for large language models in real-world scenarios, as it requires both substantial computational resources and expensive human annotation on in-domain semantic parsing data. This paper presents the first empirical study on the adversarial robustness of a prompt-based semantic parser based on Codex, a state-of-the-art (SOTA) language model trained on code. Our results demonstrate that the large language model of code is vulnerable to carefully crafted adversarial examples. To overcome this challenge, we propose methods for enhancing robustness without requiring substantial amounts of labelled data or intensive computational resources.

On Robustness of Prompt-based Semantic Parsing with Large Pre-trained Language Model: An Empirical Study on Codex

We report on an experiment in case outcome classification on European Court of Human Rights cases where our model first learns to identify the convention articles allegedly violated by the state from case facts descriptions, and subsequently uses that information to classify whether the court finds a violation of those articles. We assess the dependency between these two tasks at the feature and outcome level. Furthermore, we leverage a hierarchical contrastive loss to pull together article-specific representations of cases at the higher level, leading to distinctive article clusters. The cases in each article cluster are further pulled closer based on their outcome, leading to subclusters of cases with similar outcomes. Our experiment results demonstrate that, given a static pre-trained encoder, our models produce a small but consistent improvement in classification performance over single-task and joint models without contrastive loss.


Exploiting Summarization Data to Help Text Simplification

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Sentiment as an Ordinal Latent Variable