Papers by Jennifer Foster
Reference-Free Evaluation of Taxonomies (2026.findings-acl)
Copied to clipboard
| Challenge: | Taxonomies are used to classify items, ideas or organisms based on shared characteristics. |
| Approach: | They introduce two reference-free metrics for quality evaluation of taxonomies in the absence of labels. |
| Outcome: | The proposed metrics correlate well with F1 against ground truth taxonomies on five taxonomies and improve hierarchical classification when used with label hierarchies. |
How to Make Neural Natural Language Generation as Reliable as Templates in Task-Oriented Dialogue (2020.emnlp-main)
Copied to clipboard
| Challenge: | Neural Natural Language Generation (NLG) systems are well known for their unreliability. |
| Approach: | They propose a data augmentation approach which restricts the output of a neural network and guarantees reliability. |
| Outcome: | The proposed approach scored 100% in semantic accuracy on the E2E NLG Challenge dataset, the same as a template system. |
Cross-lingual Parsing with Polyglot Training and Multi-treebank Learning: A Faroese Case Study (D19-61)
Copied to clipboard
| Challenge: | Cross-lingual dependency parsing involves transferring syntactic knowledge from one language to another. |
| Approach: | They compare two approaches to cross-lingual dependency parsing using monolingual source models and a polyglot model which is trained on the combination of all source languages. |
| Outcome: | The proposed methods improve low-resource dependency parsers by transferring syntactic knowledge from one language to another. |
Improving Document-Level Sentiment Analysis with User and Product Context (2020.coling-main)
Copied to clipboard
| Challenge: | Existing work that improves document-level sentiment analysis by encoding user and product information has been limited to considering only the text of the current review. |
| Approach: | They propose to incorporate all available historical review text belonging to the author of the review in question and investigate the inclusion of his- torical reviews associated with the current product. |
| Outcome: | The proposed model improves on IMDB, Yelp 2013 and Yelpan 2014 datasets by more than 2 percentage points in the best case. |
Shape of Synth to Come: Why We Should Use Synthetic Data for English Surface Realization (2020.acl-main)
Copied to clipboard
| Challenge: | In the Surface Realization Shared Tasks of 2018 and 2019, there was little difference in absolute performance between systems trained with and without synthetic data. |
| Approach: | They propose to use synthetic data to explore approaches to surface realization from Universal-Dependency-like trees to surface strings for several languages. |
| Outcome: | The proposed method improves performance of a previously state-of-the-art system by 8 BLEU points over the previous system on the English dataset. |
Investigating the Saliency of Sentiment Expressions in Aspect-Based Sentiment Analysis (2023.findings-acl)
Copied to clipboard
| Challenge: | In a set of masking experiments, we examine the extent to which the tokens identified as salient by LIME and a gradient-based method are being used by the classifier. |
| Approach: | They use a BERT BASE model to mask the sentiments of an English dataset and find that both methods produce faithful rationales. |
| Outcome: | The proposed classifier outperforms both the gradient-based and black-box saliency methods on the SemEval 2016 english dataset. |
FarExStance: Explainable Stance Detection for Farsi (2025.coling-main)
Copied to clipboard
Majid Zarharan, Maryam Hashemi, Malika Behroozrazegh, Sauleh Eetemadi, Mohammad Taher Pilehvar, Jennifer Foster
| Challenge: | FarExStance is a new dataset for explainable stance detection in Farsi . it contains extractive explanations as evidence for stance labels and claims . |
| Approach: | They propose a dataset for explainable stance detection in Farsi with extractive explanations as evidence. |
| Outcome: | The proposed model is the most accurate on stance detection, while the best explanation is from few-shot Claude-3.5-Sonnet. |
Out-of-Distribution Generalization in Natural Language Processing: Past, Present, and Future (2023.emnlp-main)
Copied to clipboard
Linyi Yang, Yaoxian Song, Xuan Ren, Chenyang Lyu, Yidong Wang, Jingming Zhuo, Lingqiao Liu, Jindong Wang, Jennifer Foster, Yue Zhang
| Challenge: | Existing literature on the generalization of machine learning models to out-of-distribution data is lacking. |
| Approach: | They propose to present the first comprehensive review of recent progress, methods, and evaluations on the generalization challenge from an OOD perspective in natural language understanding. |
| Outcome: | The proposed survey provides the first comprehensive review of recent progress, methods, and evaluations on the generalization challenge from an OOD perspective in natural language understanding. |
Semantic-Aware Dynamic Retrospective-Prospective Reasoning for Event-Level Video Question Answering (2023.acl-srw)
Copied to clipboard
| Challenge: | Event-Level Video Question Answering (EVQA) requires complex reasoning across video events to obtain the visual information needed to provide optimal answers. |
| Approach: | They propose a semantic-aware dynamic retrospective-prospective reasoning approach for video-based question answering that explicitly uses the Semantic Role Labeling (SRL) structure of the question in the dynamic reasoning process. |
| Outcome: | The proposed model outperforms existing models on a trafficQA benchmark dataset. |
Exploiting Rich Textual User-Product Context for Improving Personalized Sentiment Analysis (2023.findings-acl)
Copied to clipboard
| Challenge: | Typical approaches do not exploit the potential of historical reviews or do not make full use of user/product associations. |
| Approach: | They propose to use historical reviews to initialize user and product representations and incorporate textual associations via a user-product cross-context module. |
| Outcome: | The proposed method outperforms existing state-of-the-art models on IMDb, Yelp and Longformer benchmarks. |
gaBERT — an Irish Language Model (2022.lrec-1)
Copied to clipboard
James Barry, Joachim Wagner, Lauren Cassidy, Alan Cowap, Teresa Lynn, Abigail Walsh, Mícheál J. Ó Meachair, Jennifer Foster
| Challenge: | We compare gaBERT to multilingual BERT and the monolingual Irish WikiBERT and show that gaBERt provides better representations for downstream parsing tasks. |
| Approach: | They propose a monolingual BERT model for the Irish language that provides better representations for a downstream parsing task. |
| Outcome: | The proposed model performs better than the multilingual BERT and the monolingual Irish WikiBERT on a parsing task. |
FoodTaxo: Generating Food Taxonomies with Large Language Models (2025.acl-industry)
Copied to clipboard
| Challenge: | a recent study shows that LLMs are useful for automating taxonomies from a seed taxonomy to a set of known concepts. |
| Approach: | They propose to use Large Language Models for automated taxonomy generation and completion. |
| Outcome: | The proposed approach is based on an open-source LLM (Llama-3). |
Revisiting Tri-training of Dependency Parsers (2021.emnlp-main)
Copied to clipboard
| Challenge: | Pre-trained word embeddings and self-training have been used in dependency parsing tasks for years. |
| Approach: | They compare tri-training and pretrained word embeddings in dependency parsing . they use language-specific FastText and ELMo embedds and multilingual BERT embedders . |
| Outcome: | The proposed methods are tri-training and pretrained word embeddings. |
Treebank Embedding Vectors for Out-of-Domain Dependency Parsing (2020.acl-main)
Copied to clipboard
| Challenge: | a recent advance in monolingual dependency parsing is the idea of a treebank embedding vector . this allows the model to prefer training data from one treebank over another at test time . |
| Approach: | They propose a method to predict a treebank vector for sentences that do not come from a particular treebank . they also explore what happens when they move away from predefined treebank embedding vectors . |
| Outcome: | The proposed method can predict treebank vectors for sentences that do not come from a treebank used in training with sufficient accuracy for nine out of ten languages. |
Do Stochastic Parrots have Feelings Too? Improving Neural Detection of Synthetic Text via Emotion Recognition (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Recent advances in generative AI have shone a spotlight on high-performance synthetic text generation technologies. |
| Approach: | They propose to use emotion-driven pretrained language models to generate synthetic text that lacks emotional coherence. |
| Outcome: | The proposed detector achieves significant improvements across a range of synthetic text generators, various sized models, datasets, and domains. |
Is It Dish Washer Safe? Automatically Answering “Yes/No” Questions Using Customer Reviews (N19-3)
Copied to clipboard
| Challenge: | Using Amazon reviews, we find that the answer to a question is only in 45% of cases. |
| Approach: | They combine Amazon reviews with consumer reviews and manually analyse 400 questions from four domains to find that reviews directly contain the answer to the question . they then compare QA systems that use reviews in addition to the questions to see if they can be useful for other question types. |
| Outcome: | The proposed system outperforms the chance baseline but not by a large margin. |
English Machine Reading Comprehension Datasets: A Survey (2021.emnlp-main)
Copied to clipboard
| Challenge: | a survey of English Machine Reading Comprehension datasets is carried out . the aim is to provide a concise yet informative overview of the landscape . |
| Approach: | They survey 60 English Machine Reading Comprehension datasets to provide a resource for other researchers interested in this problem. |
| Outcome: | The proposed survey covers 60 English MRC datasets with a view to providing a resource for other researchers interested in the problem. |
Fact or Factitious? Contextualized Opinion Spam Detection (P19-2)
Copied to clipboard
| Challenge: | In analytic analysis of fake reviews, we compare a number of techniques to detect them. |
| Approach: | They propose a machine learning approach that fine-tunes contextualised embeddings to detect fake reviews. |
| Outcome: | The proposed approach fine-tunes state of the art contextualised embeddings to show that it is effective in detecting fake reviews and lay the groundwork for future research in this area. |
Improving Unsupervised Question Answering via Summarization-Informed Question Generation (2021.emnlp-main)
Copied to clipboard
| Challenge: | Question Generation (QG) is the production of meaningful questions given a set of input passages and corresponding answers. |
| Approach: | They propose a method which uses questions generated heuristically from news summaries as a source of training data for a QG system. |
| Outcome: | The proposed method outperforms previous unsupervised models on three in-domain datasets and three out-of-domain ones. |
TwittIrish: A Universal Dependencies Treebank of Tweets in Modern Irish (2022.acl-long)
Copied to clipboard
| Challenge: | Modern Irish is a minority language lacking computational resources for accurate automatic syntactic parsing of user-generated content. |
| Approach: | They propose to use a treebank to facilitate natural language parsing of user-generated content in Irish. |
| Outcome: | The proposed treebank enables natural language processing of user-generated content in Irish. |