Papers with symbolic representations
Contrastive Explanations of Text Classifiers as a Service (2022.naacl-demo)
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| Challenge: | ContrXT provides time contrastive explanations of black box text classifiers by manipulating binary decision diagrams. |
| Approach: | They propose a system that provides time contrastive explanations of black box classifiers as a service by manipulating binary decision diagrams. |
| Outcome: | The proposed system has a throughput of 2.55 users per second and is available as a python pip package. |
Thesis Proposal: On the Granularity-Robustness Trade-off in Text-Derived Knowledge Graphs (2026.acl-srw)
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| Challenge: | Retrieval-augmented generation (RAG) based on dense embeddings is a dominant paradigm for text retrieval, but many real-world applications require attribute-specific querying. |
| Approach: | They propose a query-driven framework for constructing and retrieving knowledge graphs from text using dense embeddings. |
| Outcome: | The proposed framework combines the robustness of dense retrieval with the explicit queryability of symbolic representations. |
Grammar-Constrained Decoding Makes Large Language Models Better Logical Parsers (2025.acl-industry)
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| Challenge: | Large Language Models (LLMs) have shown capabilities in various natural language processing tasks, yet struggle with logical reasoning. |
| Approach: | They propose to combine Large Language Models with symbolic reasoners to improve syntactic correctness and semantic accuracy in logical parsing tasks. |
| Outcome: | The proposed approach improves syntactic correctness and semantic accuracy in logical parsing tasks. |
Unit Testing for Concepts in Neural Networks (2022.tacl-1)
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| Challenge: | Existing theories of language and cognition hold that these representations are structured in a compositional way and that the meanings of composite concepts (''gray car'') are inherited predictably from the meaning of the parts. |
| Approach: | They propose to test models for determining whether a system’s behavior is consistent with several key aspects of Fodor’s criteria. |
| Outcome: | The proposed models succeed on tests of groundedness, modularity, and reusability of concepts, but important questions about causality remain open. |
Efficient Text-based Reinforcement Learning by Jointly Leveraging State and Commonsense Graph Representations (2021.acl-short)
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Keerthiram Murugesan, Mattia Atzeni, Pavan Kapanipathi, Kartik Talamadupula, Mrinmaya Sachan, Murray Campbell
| Challenge: | Text-based games (TBGs) are useful benchmarks for evaluating progress in grounded language understanding and reinforcement learning (RL). |
| Approach: | They propose an agent that induces a graph representation of the game state and jointly grounds it with a commonsense knowledge from ConceptNet. |
| Outcome: | The proposed agent outperforms baseline agents in the proposed game . |
Language Models can be Deductive Solvers (2024.findings-naacl)
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| Challenge: | Recent advances have enabled Large Language Models (LLMs) to potentially exhibit reasoning capabilities, but complex logical reasoning remains a challenge. |
| Approach: | They propose a novel language model that internalizes and emulates the reasoning processes of logical solvers and avoids parsing errors by learning strict adherence to solver syntax and grammar. |
| Outcome: | The proposed model outperforms state-of-the-art solver-augmented language models and few-shot prompting methods on public deductive reasoning benchmarks. |
Hidden Schema Networks (2023.acl-long)
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| Challenge: | Existing models that encode rich semantic and syntactic content are biased, but they are effective at encoding symbolic representations. |
| Approach: | They propose a neural language model that enforces explicit relational structures which allow for compositionality onto the output representations of pretrained language models. |
| Outcome: | The proposed model can encode sentences into sequences of symbols and infer the posterior distribution of the model from natural language datasets. |
A Survey of AMR Applications (2024.emnlp-main)
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| Challenge: | Abstract Meaning Representation (AMR) is a semantic representation that takes the form of a rooted, directed graph. |
| Approach: | They analyze more than 100 papers which use Abstract Meaning Representation (AMR) they highlight the range of applications for which AMR has been harnessed and techniques for incorporating it . they also highlight broader AMR engineering patterns and outline areas of future work that seem ripe for AMR incorporation. |
| Outcome: | The results highlight the range of applications for which AMR has been harnessed and the techniques for incorporating it into those applications. |
SenticNet 7: A Commonsense-based Neurosymbolic AI Framework for Explainable Sentiment Analysis (2022.lrec-1)
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| Challenge: | Despite recent advances, AI still struggles with complex tasks that require commonsense reasoning such as natural language understanding. |
| Approach: | They propose a commonsense-based framework that aims to overcome these limitations in the context of sentiment analysis. |
| Outcome: | The proposed framework overcomes these limitations in the context of sentiment analysis. |
Learning Bidirectional Morphological Inflection like Humans (2024.lrec-main)
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| Challenge: | Recent research has focused on whether neural models can acquire morphological inflection like humans. |
| Approach: | They propose to use a recurrent neural network with attention and the transformer to train a symbolic model under a human-like learning environment to evaluate their models. |
| Outcome: | The proposed models did not accurately inflect verbs in the same manner as humans in terms of morphological inflection direction. |
CoMAT: Chain of Mathematically Annotated Thought Improves Mathematical Reasoning (2025.emnlp-main)
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| Challenge: | Mathematical reasoning remains a significant challenge for large language models (LLMs), despite advances in prompting techniques such as Chain-of-Thought (CoT). |
| Approach: | They propose a framework that enhances reasoning through two stages: Symbolic Conversion and Reasoning Execution. |
| Outcome: | The proposed framework outperforms traditional CoT on six out of seven benchmarks across four LLMs. |
Enhancing Logical Reasoning in Language Models via Symbolically-Guided Monte Carlo Process Supervision (2025.emnlp-main)
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| Challenge: | Large language models have shown strong performance in many reasoning benchmarks, but lack robust planning or symbolic abstractions. |
| Approach: | They propose to synthesize high-quality symbolic reasoning trajectories with stepwise pseudo-labels at scale via Monte Carlo estimation. |
| Outcome: | The proposed method can be trained on high-quality symbolic reasoning trajectories with stepwise pseudo-labels at scale using Monte Carlo estimation. |