| 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. |
Similar Papers
When Truth Matters - Addressing Pragmatic Categories in Natural Language Inference (NLI) by Large Language Models (LLMs) (2023.starsem-1)
Copied to clipboard
| Challenge: | In this paper, we examine the ability of large language models (LLMs) to accommodate different pragmatic sentence types, such as questions, commands, and sentence fragments for natural language inference (NLI). |
| Approach: | They propose to fine-tune large language models to accommodate different sentence types for natural language inference (NLI) they also explore ChatGPT's concept of entailment by using a symbolic semantic parser. |
| Outcome: | The proposed models can accommodate different sentence types without losing too much accuracy on MNLI-matched models. |
One Semantic Parser to Parse Them All: Sequence to Sequence Multi-Task Learning on Semantic Parsing Datasets (2021.starsem-1)
Copied to clipboard
| Challenge: | Existing semantic parsing datasets lack a single standard for meaning representations . lack of a standard led to the creation of plethora of datasets requiring expert annotators . |
| Approach: | They propose to use multi-task learning to unify different datasets and train a single model for them. |
| Outcome: | The proposed architectures yield better parsing accuracies and composition generalization than single-task models. |
Exploring Factual Entailment with NLI: A News Media Study (2024.starsem-1)
Copied to clipboard
| Challenge: | Recent studies have focused on the relationship between factuality and Natural Language Inference (NLI). |
| Approach: | They propose a novel annotation scheme that models factual rather than textual entailment and use it to annotate a dataset of naturally occurring sentences from news articles. |
| Outcome: | The proposed annotation scheme can be used to model factual relationships on a dataset of naturally occurring sentences from news articles. |
A Visuospatial Dataset for Naturalistic Verb Learning (2020.starsem-1)
Copied to clipboard
| Challenge: | a new dataset is available for training and evaluating grounded language models . our data is designed to emulate the quality of language data a pre-verbal child would have access to . |
| Approach: | They propose a dataset for training and evaluating grounded language models . they use naturalistic, spontaneous speech paired with richly grounded visuospatial context . |
| Outcome: | The proposed dataset compares two distributional semantics models with one that does not. |
Polysemy through the lens of psycholinguistic variables: a dataset and an evaluation of static and contextualized language models (2024.starsem-1)
Copied to clipboard
| Challenge: | Polysemes are words that can have different senses depending on context . traditionally, NLP models assume that each sense should be given a separate representation in a lexicon, thus limiting the amount of evidence that can be gained from their use. |
| Approach: | They propose a framework to model polysemes as a continuous variation in psycholinguistic properties of a word in context without postulating jumps between senses. |
| Outcome: | The proposed framework accommodates different sense interpretations, without postulating clear-cut jumps between senses. |
Compositional generalization with a broad-coverage semantic parser (2022.starsem-1)
Copied to clipboard
| Challenge: | Recent work has shown that compositional generalization on COGS is difficult and complex. |
| Approach: | They propose a compositional semantic parser that solves compositional generalization on COGS dataset. |
| Outcome: | The AM parser solves compositional generalization on the COGS dataset. |
Investigating Aspect Features in Contextualized Embeddings with Semantic Scales and Distributional Similarity (2024.starsem-1)
Copied to clipboard
| Challenge: | Aspect is a linguistic category describing how actions and events unfold over time. |
| Approach: | They propose to use semantic projections to examine whether the vector dimensions of annotated verbs reflect human linguistic distinctions. |
| Outcome: | The proposed models encode the aspects of stativity, durativity and telicity in most of their layers, while durativité is the most challenging feature. |
Empirical Sufficiency Lower Bounds for Language Modeling with Locally-Bootstrapped Semantic Structures (2023.starsem-1)
Copied to clipboard
| Challenge: | a recent attempt at language modeling with predicted semantic structure failed to establish empirical lower bounds on what could have made the attempt successful. |
| Approach: | They propose a concise binary vector representation of semantic structure at the lexical level and evaluate how good an incremental tagger needs to be to achieve better-than-baseline performance. |
| Outcome: | The proposed model can achieve better-than-baseline performance without losing its main advantages and lower bounds on prediction quality can't be established via a single score alone. |
Event Semantic Knowledge in Procedural Text Understanding (2023.starsem-1)
Copied to clipboard
| Challenge: | Annotators’ reliance on commonsense knowledge to annotate implicit state information is a challenge for entity state tracking. |
| Approach: | They propose a method for entity state tracking that incorporates commonsense entity-centric knowledge from ConceptNet into a BERT-based neural-symbolic architecture. |
| Outcome: | The proposed model outperforms existing models on the ProPara dataset and is domain-agnostic. |
Did the Cat Drink the Coffee? Challenging Transformers with Generalized Event Knowledge (2021.starsem-1)
Copied to clipboard
Paolo Pedinotti, Giulia Rambelli, Emmanuele Chersoni, Enrico Santus, Alessandro Lenci, Philippe Blache
| Challenge: | Prior work has explored the ability of computational models to predict word semantic fit with a given predicate. |
| Approach: | They compare Transformers Language Models to SDM to assess their performance . they found that TLMs do not capture important aspects of event knowledge . people can discriminate between typical and atypical events, they say . |
| Outcome: | The proposed models can achieve comparable performance to SDM, but they lack important aspects of event knowledge. |