Papers by Sam Thomson
Learning Joint Semantic Parsers from Disjoint Data (N18-1)
Copied to clipboard
| Challenge: | Various formal meaning representations have been developed corresponding to different semantic theories. |
| Approach: | They propose a method to learn a semantic parser from multiple datasets by treating annotations for unobserved formalisms as latent structured variables. |
| Outcome: | The proposed approach improves on existing methods using unobserved formalisms and underlying corpora. |
Task-Oriented Dialogue as Dataflow Synthesis (2020.tacl-1)
Copied to clipboard
Jacob Andreas, John Bufe, David Burkett, Charles Chen, Josh Clausman, Jean Crawford, Kate Crim, Jordan DeLoach, Leah Dorner, Jason Eisner, Hao Fang, Alan Guo, David Hall, Kristin Hayes, Kellie Hill, Diana Ho, Wendy Iwaszuk, Smriti Jha, Dan Klein, Jayant Krishnamurthy, Theo Lanman, Percy Liang, Christopher H. Lin, Ilya Lintsbakh, Andy McGovern, Aleksandr Nisnevich, Adam Pauls, Dmitrij Petters, Brent Read, Dan Roth, Subhro Roy, Jesse Rusak, Beth Short, Div Slomin, Ben Snyder, Stephon Striplin, Yu Su, Zachary Tellman, Sam Thomson, Andrei Vorobev, Izabela Witoszko, Jason Wolfe, Abby Wray, Yuchen Zhang, Alexander Zotov
| Challenge: | Existing approaches to task-oriented dialogue represent dialogue state as a dataflow graph . microsoft's SMCalFlow dataset features complex dialogues about events, weather, places, and people . |
| Approach: | They propose a dataflow graph-based dialogue agent that maps each user utterance to a program that extends this graph. |
| Outcome: | The proposed framework improves representability and predictability in natural dialogues . it uses dataflow graphs and metacomputation to map user intents to a program . |
Syntactic Scaffolds for Semantic Structures (D18-1)
Copied to clipboard
| Challenge: | Syntactic scaffolds avoid expensive syntactical processing at runtime . many systems have used syntastic dependency or phrase-based parsers as preprocessing for semantic analysis. |
| Approach: | They propose a multitask learning approach that uses a syntactic treebank to integrate syntaktic information into semantic tasks. |
| Outcome: | The proposed method improves on PropBank semantics, frame semantics and coreference resolution tasks. |
Language-to-Code Translation with a Single Labeled Example (2024.emnlp-main)
Copied to clipboard
Kaj Bostrom, Harsh Jhamtani, Hao Fang, Sam Thomson, Richard Shin, Patrick Xia, Benjamin Van Durme, Jason Eisner, Jacob Andreas
| Challenge: | In-Context Inverse Programming (ICIP) bootstraps a language-to-code system using mostly unlabeled programs written using a potentially unfamiliar library or API. |
| Approach: | They propose a method for bootstrapping a language-to-code system using mostly unlabeled programs written using a potentially unfamiliar library or API. |
| Outcome: | The proposed method outperforms baselines across nine domains and 100 examples in a “nearly unsupervised” setting. |
Compositional Generalization for Neural Semantic Parsing via Span-level Supervised Attention (2021.naacl-main)
Copied to clipboard
Pengcheng Yin, Hao Fang, Graham Neubig, Adam Pauls, Emmanouil Antonios Platanios, Yu Su, Sam Thomson, Jacob Andreas
| Challenge: | Existing approaches to compositional generalization in semantic parsers focus on word-level alignments, but they focus on spans. |
| Approach: | They propose a span-level supervised attention loss that improves compositional generalization in semantic parsers by focusing on spans. |
| Outcome: | The proposed method improves on three benchmarks of compositional generalization. |
Online Semantic Parsing for Latency Reduction in Task-Oriented Dialogue (2022.acl-long)
Copied to clipboard
| Challenge: | Standard conversational semantic parsing maps a user's intent into an executable program, but execution is slow when expensive function calls are included. |
| Approach: | They propose a task of online semantic parsing to predict and execute function calls while the user is still speaking. |
| Outcome: | The proposed approach reduces latency with good parsing quality and execution cost. |
Bridging CNNs, RNNs, and Weighted Finite-State Machines (P18-1)
Copied to clipboard
| Challenge: | recurrent and convolutional neural networks are useful for encoding natural language utterances. |
| Approach: | They propose a model that combines neural representation learning with weighted finite-state automatas to learn a soft version of traditional surface patterns. |
| Outcome: | The proposed model is comparable or better than a BiLSTM baseline and a CNN baseline on three text classification tasks. |
Value-Agnostic Conversational Semantic Parsing (2021.acl-long)
Copied to clipboard
Emmanouil Antonios Platanios, Adam Pauls, Subhro Roy, Yuchen Zhang, Alexander Kyte, Alan Guo, Sam Thomson, Jayant Krishnamurthy, Jason Wolfe, Jacob Andreas, Dan Klein
| Challenge: | Existing models rely on rich representations of dialogue history that include all previously generated components of the output. |
| Approach: | They propose a model that abstracts over values to focus prediction on type- and function-level context. |
| Outcome: | The proposed model outperforms baseline models by 7.3% and 10.6% on SMCalFlow and TreeDST datasets. |
Backpropagating through Structured Argmax using a SPIGOT (P18-1)
Copied to clipboard
| Challenge: | Structured projection of intermediate gradients (SPIGOT) is a new method for backpropagating through neural networks . structure-based learning methods for natural language processing are increasingly dominated by end-to-end differentiable functions . |
| Approach: | They propose a structured projection of intermediate gradients method for backpropagating through neural networks that includes hard-decision structured predictions in intermediate layers. |
| Outcome: | The proposed method improves on two structured NLP pipelines: syntactic-then-semantic dependency parsing and semantic parser followed by sentiment classification. |
MICE for CATs: Model-Internal Confidence Estimation for Calibrating Agents with Tools (2025.naacl-long)
Copied to clipboard
| Challenge: | Existing models that measure confidence in tool-using agents are poorly calibrated. |
| Approach: | They propose a class of model-internal confidence estimators to assess confidence when calling tools. |
| Outcome: | The proposed class beats baselines on a simulated trial and error (STE) tool-calling dataset using Llama3 models. |
Constrained Language Models Yield Few-Shot Semantic Parsers (2021.emnlp-main)
Copied to clipboard
Richard Shin, Christopher Lin, Sam Thomson, Charles Chen, Subhro Roy, Emmanouil Antonios Platanios, Adam Pauls, Dan Klein, Jason Eisner, Benjamin Van Durme
| Challenge: | Large pretrained language models excel at generating natural language, but they are not efficient for task specific semantic parsing. |
| Approach: | They propose to use large pretrained language models as few-shot semantic parsers . they paraphrase inputs into a controlled sublanguage resembling English . |
| Outcome: | The proposed model can generate surprisingly accurate models on multiple tasks with minimal code and data. |
Toward Interactive Dictation (2023.acl-long)
Copied to clipboard
| Challenge: | Existing systems that allow both dictation and editing-by-voice restrict their command language to flat templates invoked by trigger words. |
| Approach: | They propose to allow users to interrupt dictation with spoken editing commands in open-ended natural language. |
| Outcome: | The proposed system can predict edited text with large pre-trained models and predict small programs. |
When More Data Hurts: A Troubling Quirk in Developing Broad-Coverage Natural Language Understanding Systems (2022.emnlp-main)
Copied to clipboard
Elias Stengel-Eskin, Emmanouil Antonios Platanios, Adam Pauls, Sam Thomson, Hao Fang, Benjamin Van Durme, Jason Eisner, Yu Su
| Challenge: | In natural language understanding systems, users’ evolving needs necessitate the addition of new features over time, indexed by new symbols added to the meaning representation space. |
| Approach: | They propose to use a small set of new symbols to build broad-coverage NLU systems. |
| Outcome: | The proposed model is based on two prototypical NLU tasks: intent recognition and semantic parsing. |
Rational Recurrences (D18-1)
Copied to clipboard
| Challenge: | Recent studies show that neural models lack strong intuitions . recent studies show connections between convolutional neural networks and weighted finite state automata (WFSAs) |
| Approach: | They show that some recurrent neural networks share a connection to weighted finite state automata (WFSAs) they define rational recurrences as recursive hidden state update functions . they propose to use these functions to write forward calculations of a finite set of WFSA's . |
| Outcome: | The proposed model outperforms two baselines on language modeling and text classification. |
Guided K-best Selection for Semantic Parsing Annotation (2022.acl-demo)
Copied to clipboard
Anton Belyy, Chieh-yang Huang, Jacob Andreas, Emmanouil Antonios Platanios, Sam Thomson, Richard Shin, Subhro Roy, Aleksandr Nisnevich, Charles Chen, Benjamin Van Durme
| Challenge: | a prototype model trained on a small amount of data is not available, leading to limited prediction performance. |
| Approach: | They propose a human-in-the-loop process that generates a set of valid candidates and allows users to quickly traverse the set and filter incorrect parses. |
| Outcome: | The proposed process can be used to efficiently traverse the candidate set and select the correct parse, with minimal modification when necessary. |