Papers by Chris Pal
On Extractive and Abstractive Neural Document Summarization with Transformer Language Models (2020.emnlp-main)
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| Challenge: | We present a method to produce abstractive summaries of documents that exceed several thousand words . we compare transformer based methods to extractive methods, but extractive models score higher . |
| Approach: | They propose a method to generate abstractive summaries of documents that exceed several thousand words via neural abstractive summary. |
| Outcome: | The proposed method produces abstractive summaries of documents that exceed several thousand words . it is compared with baseline methods, state-of-the-art models and variants of the proposed method . |
Internal states before wait modulate reasoning patterns (2025.findings-emnlp)
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| Challenge: | Prior work has shown that a significant driver of performance in reasoning models is their ability to reason and self-correct. |
| Approach: | They address the question whether model’s latents preceding wait tokens contain relevant information for modulating the subsequent reasoning process. |
| Outcome: | The proposed model's latents preceding wait tokens contain relevant information for modulating the subsequent reasoning process. |
Interactive Language Learning by Question Answering (D19-1)
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| Challenge: | Existing machine reading comprehension tasks lack interactive information-seeking component of comprehension. |
| Approach: | They propose a question-asking task that asks questions in a text-based environment . they propose QAit, which uses a game generator to build models that include deep reinforcement learning agents. |
| Outcome: | The proposed task poses questions about existence, location, and attributes of objects found in environment. |
Interactive Machine Comprehension with Information Seeking Agents (2020.acl-main)
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| Challenge: | Existing machine reading comprehension (MRC) models do not scale effectively to real-world applications like web-level information retrieval and question answering (QA). |
| Approach: | They propose a method that reframes existing machine reading comprehension (MRC) datasets as interactive, partially observable environments. |
| Outcome: | The proposed method "occludes" the majority of a document’s text and adds context-sensitive commands that reveal "glimpses" of the hidden text to a model. |
Would you Rather? A New Benchmark for Learning Machine Alignment with Cultural Values and Social Preferences (2020.acl-main)
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| Challenge: | Existing studies on optimal decision-making are limited and only consider individuals in isolation. |
| Approach: | They propose a task and corpus for learning alignments between machine and human preferences based on a gamified voting game . |
| Outcome: | The proposed task and corpus show that current state-of-the-art NLP models still leave much room for improvement. |
DuoRAT: Towards Simpler Text-to-SQL Models (2021.naacl-main)
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| Challenge: | Recent text-to-SQL models can translate natural language questions to corresponding SQL queries on unseen databases. |
| Approach: | They propose a re-implementation of the RAT-SQL model that uses only relation-aware or vanilla transformers as the building blocks. |
| Outcome: | The proposed model is based on the spider dataset and shows it can be used on large databases without human intervention. |
Do Neural Dialog Systems Use the Conversation History Effectively? An Empirical Study (P19-1)
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| Challenge: | Neural generative models are becoming more popular when building conversational agents. |
| Approach: | They propose to study the sensitivity of neural dialog models to unnatural perturbations . they experiment with 10 different types of perturbations on 4 multi-turn dialog datasets . |
| Outcome: | The proposed model is sensitive to unnatural changes or perturbations on 4 multi-turn dialog datasets. |