skip to main content
US FlagAn official website of the United States government
dot gov icon
Official websites use .gov
A .gov website belongs to an official government organization in the United States.
https lock icon
Secure .gov websites use HTTPS
A lock ( lock ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites.

Explore Research Products in the PAR
It may take a few hours for recently added research products to appear in PAR search results.


Title: Time-Stamped Language Model: Teaching Language Models to Understand The Flow of Events
Tracking entities throughout a procedure de- scribed in a text is challenging due to the dy- namic nature of the world described in the pro- cess. Firstly, we propose to formulate this task as a question answering problem. This en- ables us to use pre-trained transformer-based language models on other QA benchmarks by adapting those to the procedural text un- derstanding. Secondly, since the transformer- based language models cannot encode the flow of events by themselves, we propose a Time- Stamped Language Model (TSLM model) to encode event information in LMs architec- ture by introducing the timestamp encoding. Our model evaluated on the Propara dataset shows improvements on the published state- of-the-art results with a 3.1% increase in F1 score. Moreover, our model yields better re- sults on the location prediction task on the NPN-Cooking dataset. This result indicates that our approach is effective for procedural text understanding in general.  more » « less
Award ID(s):
2028626
PAR ID:
10227087
Author(s) / Creator(s):
;
Date Published:
Journal Name:
The 2021 Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL-2021)
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. ; ; ; (, Proceedings of the AAAI Conference on Artificial Intelligence)
    Transformer language models have made tremendous strides in natural language understanding tasks. However, the complexity of natural language makes it challenging to ascertain how accurately these models are tracking the world state underlying the text. Motivated by this issue, we consider the task of language modeling for the game of chess. Unlike natural language, chess notations describe a simple, constrained, and deterministic domain. Moreover, we observe that the appropriate choice of chess notation allows for directly probing the world state, without requiring any additional probing-related machinery. We find that: (a) With enough training data, transformer language models can learn to track pieces and predict legal moves with high accuracy when trained solely on move sequences. (b) For small training sets providing access to board state information during training can yield significant improvements. (c) The success of transformer language models is dependent on access to the entire game history i.e. “full attention”. Approximating this full attention results in a significant performance drop. We propose this testbed as a benchmark for future work on the development and analysis of transformer language models. 
    more » « less
  2. ; ; ; ; ; ; (, Proceedings of Machine Learning Research)
    Faust, Aleksandra; Hsu, David; Neumann, Gerhard (Ed.)
    Enabling human operators to interact with robotic agents using natural language would allow non-experts to intuitively instruct these agents. Towards this goal, we propose a novel Transformer-based model which enables a user to guide a robot arm through a 3D multi-step manipulation task with natural language commands. Our system maps images and commands to masks over grasp or place locations, grounding the language directly in perceptual space. In a suite of block rearrangement tasks, we show that these masks can be combined with an existing manipulation framework without re-training, greatly improving learning efficiency. Our masking model is several orders of magnitude more sample efficient than typical Transformer models, operating with hundreds, not millions, of examples. Our modular design allows us to leverage supervised and reinforcement learning, providing an easy interface for experimentation with different architectures. Our model completes block manipulation tasks with synthetic commands more often than a UNet-based baseline, and learns to localize actions correctly while creating a mapping of symbols to perceptual input that supports compositional reasoning. We provide a valuable resource for 3D manipulation instruction following research by porting an existing 3D block dataset with crowdsourced language to a simulated environment. Our method’s absolute improvement in identifying the correct block on the ported dataset demonstrates its ability to handle syntactic and lexical variation. 
    more » « less
  3. ; ; (, Proceedings of the Eighth International Joint Conference on Natural Language Processing)
    Mobile devices use language models to suggest words and phrases for use in text entry. Traditional language models are based on contextual word frequency in a static corpus of text. However, certain types of phrases, when offered to writers as suggestions, may be systematically chosen more often than their frequency would predict. In this paper, we propose the task of generating suggestions that writers accept, a related but distinct task to making accurate predictions. Although this task is fundamentally interactive, we propose a counterfactual setting that permits offline training and evaluation. We find that even a simple language model can capture text characteristics that improve acceptability. 
    more » « less
  4. ; (, Findings of the Association for Computational Linguistics: EMNLP 2021)
    Legal texts routinely use concepts that are difficult to understand. Lawyers elaborate on the meaning of such concepts by, among other things, carefully investigating how they have been used in the past. Finding text snippets that mention a particular concept in a useful way is tedious, time-consuming, and hence expensive. We assembled a data set of 26,959 sentences, coming from legal case decisions, and labeled them in terms of their usefulness for explaining selected legal concepts. Using the dataset we study the effectiveness of transformer models pre-trained on large language corpora to detect which of the sentences are useful. In light of models{'} predictions, we analyze various linguistic properties of the explanatory sentences as well as their relationship to the legal concept that needs to be explained. We show that the transformer-based models are capable of learning surprisingly sophisticated features and outperform the prior approaches to the task. 
    more » « less
  5. ; ; ; ; (, ISCA)
    Dialog history enhances downstream classification performance in both speech and text based dialog systems. However, there still exists a gap in dialog history integration in a fully end-to-end (E2E) spoken dialog system (SDS) versus a textual dia- log system. Text-based dialog systems use large language models (LLMs) to encode long-range dependencies by attending to the entire conversation as a contiguous token sequence. This is not possible in an E2E SDS, as speech sequences can be intractably long. We propose a convolution subsampling approach to make the speech sequence of a conversation tractable and use a conformer to attend to the speech-based conversation in a fine-grained manner. This model is further enhanced via a conversation-level knowledge transfer from a LLM using a token-level alignment strategy. Finetuning the E2E model pretrained this way gives significant gains, of up to 8%, over strong non-contextual baselines in the E2E dialog act classification task on two datasets. 
    more » « less
  • Citation Formats
  • MLA
  • APA
  • Chicago
  • BibTeX
  • Save / Share this Record
  • Send to Email