Papers by Guy Emerson
Using dependency parsing for few-shot learning in distributional semantics (2022.acl-srw)
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| Challenge: | Existing methods for few-shot learning use dependency parsing information to learn meaning of rare words based on limited amount of context sentences. |
| Approach: | They propose dependency parsing for few-shot learning to learn meaning of rare words . they use word embedding models as background spaces for few shot learning . |
| Outcome: | The proposed methods enhance the additive baseline model by using dependencies. |
Bad Form: Comparing Context-Based and Form-Based Few-Shot Learning in Distributional Semantic Models (D19-61)
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| Challenge: | Word embeddings are an essential component of many natural language processing applications. |
| Approach: | They propose 3 new tasks to obtain higher-quality vectors for word embeddings . they use word forms in training data that are related to word forms themselves . |
| Outcome: | The proposed methods improve the performance of both baseline and advanced models on 4 out of 6 tasks. |
Are Embedded Potatoes Still Vegetables? On the Limitations of WordNet Embeddings for Lexical Semantics (2023.emnlp-main)
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| Challenge: | Knowledge Base Embedding (KBE) models are widely used to encode structured information from knowledge bases, including WordNet, but the evaluation task is often focused on link prediction, ignoring their semantic capabilities. |
| Approach: | They propose to evaluate the performance of Knowledge Base Embedding (KBE) models of WordNet on link prediction and their ability to encode semantic information. |
| Outcome: | The proposed model performs poorly on two semantic tasks and two downstream tasks. |
Incremental Beam Manipulation for Natural Language Generation (2021.eacl-main)
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| Challenge: | a larger beam size can lead to deteriorating performance of natural language generation systems due to model errors . performance of NLG systems can plateau or even decrease when beam sizes larger than 10 are used . |
| Approach: | They propose to rerank the output of beam search to produce a good set of hypotheses . they propose incremental beam manipulation to discarded hypothese . |
| Outcome: | The proposed method outperforms a strong reranker on the E2E and WebNLG datasets while being on par with the existing method. |
Learning Functional Distributional Semantics with Visual Data (2022.acl-long)
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| Challenge: | Functional Distributional Semantics models the meaning of a word as a binary classifier rather than a numerical vector. |
| Approach: | They propose a method to train a Functional Distributional Semantics model with grounded visual data. |
| Outcome: | The proposed model outperforms previous work on learning semantics from Visual Genome on four external evaluation datasets. |
What are the Goals of Distributional Semantics? (2020.acl-main)
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| Challenge: | a new paper examines distributional semantic models' ability to deal with semantic challenges . authors argue that assessing progress in any field requires explicit long-term goals . |
| Approach: | They propose a broad linguistic perspective to assess distributional semantic models' ability to deal with various semantic challenges. |
| Outcome: | The proposed models can handle various semantic challenges, but they need to be explicit . a top-down approach is largely bottom-up, while a bottom-down one is mainly top-up . the authors argue that the goal is unclear and that the models are not scalable . |
Autoencoding Pixies: Amortised Variational Inference with Graph Convolutions for Functional Distributional Semantics (2020.acl-main)
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| Challenge: | Functional Distributional Semantics represents the meaning of a word as a function (a binary classifier), instead of . a vector. |
| Approach: | They propose a framework which augments the generative model of Functional Distributional Semantics with a graph-convolutional neural network to perform amortised variational inference. |
| Outcome: | The proposed framework outperforms BERT, a large pre-trained language model on two tasks and outperformed other approaches. |
Investigating Cross-Linguistic Adjective Ordering Tendencies with a Latent-Variable Model (2020.emnlp-main)
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| Challenge: | Existing models of crosslinguistic adjective ordering have relied on native speakers' intuitive judgment, not corpus data. |
| Approach: | They propose a latent-variable model that can order adjectives across 24 languages . they use tools and techniques to find universal, cross-linguistic, hierarchical ordering tendencies . |
| Outcome: | The proposed model can order adjectives across 24 languages even when languages are different . similar ordering preferences have been found to apply universally across languages . |
Visual Spatial Reasoning (2023.tacl-1)
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| Challenge: | Existing benchmarks for testing vision-language models (VLMs) are not ideal as they conflate multiple sources of error and do not allow controlled analysis on specific linguistic or cognitive properties. |
| Approach: | They present a dataset containing more than 10k natural text-image pairs with 66 types of spatial relations in English (e.g., under, in front of, facing). |
| Outcome: | The proposed model fails to capture relational information in a visual question answering task and referring expression comprehension tasks. |
Functional Distributional Semantics at Scale (2023.starsem-1)
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| Challenge: | Functional Distributional Semantics is a linguistically motivated framework for modelling lexical and sentence-level semantics with truth-conditional functions using distributional information. |
| Approach: | They propose a more expressive lexical model that works over a continuous semantic space. |
| Outcome: | The proposed model improves performance and flexibility and is compatible with present-day machine learning frameworks. |