How Large Are Lions? Inducing Distributions over Quantitative Attributes (P19-1)
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| Challenge: | Current NLP systems have little knowledge about quantitative attributes of objects and events. |
| Approach: | They propose to use web data to create a resource consisting of distributions over physical quantities associated with objects, adjectives, and verbs. |
| Outcome: | The proposed method compares favorably with state-of-the-art results on existing datasets for relative comparisons of nouns and adjectives and on a new dataset. |
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Diversity, Density, and Homogeneity: Quantitative Characteristic Metrics for Text Collections (2020.lrec-1)
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| Challenge: | Existing descriptive statistics are inadequate to summarize text collections by quantitative measures. |
| Approach: | They propose a set of characteristic metrics that quantitatively measure the dispersion, sparsity, and uniformity of a text collection. |
| Outcome: | The proposed metrics are highly correlated with text classification performance of a renowned model, which could inspire future applications. |
Why is penguin more similar to polar bear than to sea gull? Analyzing conceptual knowledge in distributional models (2020.acl-srw)
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| Challenge: | Several analysis methods have been shown to be limited and are not well understood . thesis aims to understand distributional semantic representations based on linguistic data . |
| Approach: | They propose a framework for investigating the information encoded in distributional semantic models . they combine observations made on corpora with insights obtained from data manipulation experiments . |
| Outcome: | The proposed framework pairs observations made on corpora with insights obtained from data manipulation experiments. |
How Pre-trained Word Representations Capture Commonsense Physical Comparisons (D19-60)
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| Challenge: | Pre-trained word representations capture common sense on physical properties such as size and weight. |
| Approach: | They investigate whether pre-trained representations capture comparisons and find they have higher accuracy than previous approaches. |
| Outcome: | The proposed models learn a consistent ordering over all the objects in the comparisons. |
Big Generalizations with Small Data: Exploring the Role of Training Samples in Learning Adjectives of Size (D19-64)
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| Challenge: | In previous work, models have been shown to fail in generalizing to unseen adjective-noun combinations. |
| Approach: | They propose a visual reasoning task dealing with quantities that challenges models to learn the meaning of size adjectives from visually-grounded contexts. |
| Outcome: | The proposed task is based on a visual reasoning task dealing with quantities and shows that seeing some of the cases during training helps a model understand the rule subtending the task. |
Out-of-Distribution Generalization in Natural Language Processing: Past, Present, and Future (2023.emnlp-main)
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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. |
From Prejudice to Parity: A New Approach to Debiasing Large Language Model Word Embeddings (2025.coling-main)
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| Challenge: | Existing work in this field has looked most commonly into gender bias, racial bias, and religious bias. |
| Approach: | They propose an algorithm that uses a neural network to perform ‘soft debiasing’ and build on the seminal work of (CITATION) and (CitATION). |
| Outcome: | The proposed algorithm outperforms current methods on gender, race, and religion metrics on a wide range of metrics. |
Beyond Counting Datasets: A Survey of Multilingual Dataset Construction and Necessary Resources (2022.findings-emnlp)
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| Challenge: | Existing studies have examined the quality of labeled data in non-English languages. |
| Approach: | They annotate how datasets are created, input text and label sources, tools used to build them and what they study. |
| Outcome: | The results show that language-proficient NLP researchers' estimated availability correlates with dataset availability. |
Building Static Embeddings from Contextual Ones: Is It Useful for Building Distributional Thesauri? (2022.lrec-1)
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| Challenge: | contextual language models are dominant in the field of Natural Language Processing, but they are not suitable for all uses. |
| Approach: | They propose a method for building word or type-level embeddings from contextual models . they evaluate a large set of English nouns from the perspective of extracting semantic similarity relations . |
| Outcome: | The proposed method can be used to build word or type embeddings from contextual models . it can be exploited for a wide set of English nouns, showing it can improve distributional thesauri . |
Do ever larger octopi still amplify reporting biases? Evidence from judgments of typical colour (2022.aacl-short)
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| Challenge: | Language models trained on text-only corpora have no direct access to the physical world and thus suffer from reporting bias. |
| Approach: | They investigate reporting bias from the perspective of colour in larger language models such as PaLM and GPT-3. |
| Outcome: | The proposed models outperform smaller models on the basis of colour and more closely track human judgements than smaller models. |
Uncovering Bias in Large Vision-Language Models at Scale with Counterfactuals (2025.naacl-long)
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| Challenge: | Large Vision-Language Models (LVLMs) have been proposed to augment LLMs with visual inputs. |
| Approach: | They propose large vision-Language Models to augment LLMs with visual inputs. |
| Outcome: | The proposed models condition generated text on both an input image and a visual prompt, enabling a variety of use cases such as visual question answering and multimodal chat. |