Croatia

This research studies graph-based approaches for Answer Sentence Selection (AS2), an essential component for retrieval-based Question Answering (QA) systems. During offline learning, our model constructs a small-scale relevant training graph per question in an unsupervised manner, and integrates with Graph Neural Networks. Graph nodes are question sentence to answer sentence pairs. We train and integrate state-of-the-art (SOTA) models for computing scores between question-question, question-answer, and answer-answer pairs, and use thresholding on relevance scores for creating graph edges. Online inference is then performed to solve the AS2 task on unseen queries. Experiments on two well-known academic benchmarks and a real-world dataset show that our approach consistently outperforms SOTA QA baseline models.

EACL 2023

Question-Answer Sentence Graph for Joint Modeling Answer Selection

information retrieval systems

graph representation learning

open-domain question answering

**Welcome to ACL-IJCNLP 2021!**

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ACL-IJCNLP 2021

The great event is jointly organized by the Association for Computational Linguistics (ACL) and Asian Federation of Natural Language Processing (AFNLP).

technical paper

### 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.

Socratic questioning is a form of reflective inquiry often employed in education to encourage critical thinking in students, and to elicit awareness of beliefs and perspectives in a subject during therapeutic counseling. Specific types of Socratic questions are employed for enabling reasoning and alternate views against the context of individual personal opinions on a topic. Socratic contexts are different from traditional question generation contexts where "answer-seeking" questions are generated against a given formal passage on a topic, narrative stories or conversations.

We present SocratiQ, the first large dataset of 110K (question, context) pairs for enabling studies on Socratic Question Generation (SoQG). We provide an in-depth study on the various types of Socratic questions and present models for generating Socratic questions against a given context through prompt tuning. Our automated and human evaluation results demonstrate that our SoQG models can produce realistic, type-sensitive, human-like Socratic questions enabling potential applications in counseling and coaching.

Socratic Question Generation: A Novel Dataset, Models, and Evaluation

Task-oriented semantic parsing models have achieved strong results in recent years, but unfortunately do not strike an appealing balance between model size, runtime latency, and cross-domain generalizability. We tackle this problem by introducing scenario-based semantic parsing: a variant of the original task which first requires disambiguating an utterance's "scenario" (an intent-slot template with variable leaf spans) before generating its frame, complete with ontology and utterance tokens. This formulation enables us to isolate coarse-grained and fine-grained aspects of the task, each of which we solve with off-the-shelf neural modules, also optimizing for the axes outlined above. Concretely, we create a Retrieve-and-Fill (RAF) architecture comprised of (1) a retrieval module which ranks the best scenario given an utterance and (2) a filling module which imputes spans into the scenario to create the frame. Our model is modular, differentiable, interpretable, and allows us to garner extra supervision from scenarios. RAF achieves strong results in high-resource, low-resource, and multilingual settings, outperforming recent approaches by wide margins despite, using base pre-trained encoders, small sequence lengths, and parallel decoding.

Retrieve-and-Fill for Scenario-based Task-Oriented Semantic Parsing

We introduce a new in-context learning paradigm to measure Large Language Models' (LLMs) ability to learn novel words during inference. In particular, we rewrite Winograd-style co-reference resolution problems by replacing the key concept word with a synthetic but plausible word that the model must understand to complete the task. Solving this task requires the model to make use of the dictionary definition of the new word given in the prompt. This benchmark addresses word acquisition, one important aspect of the diachronic degradation known to afflict LLMs. As LLMs are frozen in time at the moment they are trained, they are normally unable to reflect the way language changes over time. We show that the accuracy of LLMs compared to the original Winograd tasks decreases radically in our benchmark, thus identifying a limitation of current models and providing a benchmark to measure future improvements in LLMs ability to do in-context learning.

WinoDict: Probing language models for in-context word acquisition

Semitic morphologically-rich languages (MRLs) are characterized by extreme word ambiguity. Because most vowels are omitted in standard texts, many of the words are homographs with multiple possible analyses, each with a different pronunciation and different morphosyntactic properties. This ambiguity goes \{\textbackslash{}em beyond\} word-sense disambiguation (WSD), and may include token segmentation into multiple word units. Previous research on MRLs claimed that standardly trained pre-trained language models (PLMs) based on word-pieces may not sufficiently capture the internal structure of such tokens in order to distinguish between these analyses.
Taking Hebrew as a case study, we investigate the extent to which Hebrew homographs can be disambiguated and analyzed using PLMs. We evaluate all existing models for contextualized Hebrew embeddings on a novel Hebrew homograph challenge sets that we deliver. Our empirical results demonstrate that contemporary Hebrew contextualized embeddings outperform non-contextualized embeddings; and that they are most effective for disambiguating segmentation and morphosyntactic features, less so regarding pure word-sense disambiguation. We show that these embeddings are more effective when the number of word-piece splits is limited, and they are more effective for 2-way and 3-way ambiguities than for 4-way ambiguity. We show that the embeddings are equally effective for homographs of both balanced and skewed distributions, whether calculated as masked or unmasked tokens. Finally, we show that these embeddings are as effective for homograph disambiguation with extensive supervised training as with a few-shot setup.

Do Pretrained Contextual Language Models Distinguish between Hebrew Homograph Analyses?

Pretrained language models (PLMs) on domain- specific data have been proven to be effec- tive for in-domain natural language process- ing (NLP) tasks. Our work aimed to develop a language model which can be effective for the NLP tasks with the data from diverse so- cial media platforms. We pretrained a lan- guage model on Twitter and Reddit posts in En- glish consisting of 929M sequence blocks for 112K steps. We benchmarked our model and 3 transformer-based models—BERT, BERTweet, and RoBERTa on 40 social media text classifi- cation tasks. The results showed that although our model did not perform the best on all of the tasks, it outperformed the baseline model— BERT on most of the tasks, which illustrates the effectiveness of our model. Also, our work provides some insights of how to improve the efficiency of training PLMs.

SocBERT: A Pretrained Model for Social Media Text

We propose a novel approach that employs token-level Levenshtein operations to learn a continuous latent space of vector representations to capture the underlying semantic information with regard to the document editing process. Though our model outperforms strong baselines when fine-tuned on edit-centric tasks, it is unclear if these results are due to domain similarities between fine-tuning and pre-training data, suggesting that the benefits of our proposed approach over regular masked language-modelling pre-training are limited.

Edit Aware Representation Learning via Levenshtein Prediction

We introduce simple randomized variants of byte pair encoding (BPE) and explore whether randomizing the selection of merge operations substantially affects a downstream machine translation task.
We focus on translation into morphologically rich languages, hypothesizing that this task may show sensitivity to the method of choosing subwords.
Analysis using a Bayesian linear model indicates that one variant performs nearly indistinguishably compared to standard BPE while the other degrades performance less than we anticipated.
We conclude that although standard BPE is widely used, there exists an interesting universe of potential variations on it worth investigating.

What changes when you randomly choose BPE merge operations? Not much.

In order to reliably process natural language, NLP systems must generalize to the long tail of rare utterances. We propose a method to create challenging benchmarks that require generalizing to the tail of the distribution by re-splitting existing datasets. We create 'Likelihood Splits' where examples that are assigned lower likelihood by a pre-trained language model (LM) are placed in the test set, and more likely examples are in the training set. This simple approach can be customized to construct meaningful train-test splits for a wide range of tasks. Likelihood Splits surface more challenges than random splits: relative error rates of state-of-the-art models increase by 59% for semantic parsing on Spider, 93% for natural language inference on SNLI, and 33% for yes/no question answering on BoolQ, on our splits compared with the corresponding random splits. Moreover, Likelihood Splits create fairer benchmarks than adversarial filtering; when the LM used to create the splits is also employed as the task model, our splits do not unfairly penalize the LM.

Benchmarking Long-tail Generalization with Likelihood Splits

The number of scientific publications in the biomedical domain is continuously increasing with time. An efficient system for indexing these publications is required to make the information accessible according to the user’s information needs. Task 10a of the BioASQ challenge aims to classify PubMed articles according to the MeSH ontology so that new publications can be grouped with similar preexisting publications in the field without the assistance of time-consuming and costly annotations by human annotators. In this work, we use Graph Neural Network (GNN) in the link prediction setting to exploit potential graph-structured information present in the dataset which could otherwise be neglected by transformer-based models. Additionally, we provide error analysis and a plausible reason for the substandard performance achieved by GNN.

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Socratic Question Generation: A Novel Dataset, Models, and Evaluation

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