United States

We propose HTCInfoMax for hierarchical text classification (HTC) by introducing information maximization which includes two modules: text-label mutual information maximization and label prior matching to address the limitations of previous SOTA model HiAGM.

NAACL 2021

HTCInfoMax: A Global Model for Hierarchical Text Classification via Information Maximization

2021 Annual Conference of the North American Chapter of the Association for Computational Linguistics.

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technical paper

Text-based games simulate worlds and interact with players using natural language. Recent work has used them as a testbed for autonomous language-understanding agents, with the motivation being that understanding the meanings of words or semantics is a key component of how humans understand, reason, and act in these worlds. However, it remains unclear to what extent artificial agents utilize semantic understanding of the text. To this end, we perform experiments to systematically reduce the amount of semantic information available to a learning agent. Surprisingly, we find that an agent is capable of achieving high scores even in the complete absence of language semantics, indicating that the currently popular experimental setup and models may be poorly designed to understand and leverage game texts. To remedy this deficiency, we propose an inverse dynamics decoder to regularize the representation space and encourage exploration, which shows improved performance on several games including ZORK I. We discuss the implications of our findings for designing future agents with stronger semantic understanding.

Reading and Acting while Blindfolded: The Need for Semantics in Text Game Agents

Recent research in Visual Question Answering (VQA) has revealed state-of-the-art models to be inconsistent in their understanding of the world -- they answer seemingly difficult questions requiring reasoning correctly but get simpler associated sub-questions wrong. These sub-questions pertain to lower level visual concepts in the image that models ideally should understand to be able to answer the reasoning question correctly.

SOrT-ing VQA Models : Contrastive Gradient Learning for Improved Consistency

**Read the paper on the folowing link:**

https://www.aclweb.org/anthology/2021.naacl-main.249/

**Abstract:**

0

Semi-Supervised Policy Initialization for Playing Games with Language Hints

We take the first step towards multilingual style transfer by creating and releasing XFORMAL, a benchmark of multiple formal reformulations of informal text in Brazilian Portuguese, French, and Italian. Results on XFOR- MAL suggest that state-of-the-art style transfer approaches perform close to simple baselines, indicating that style transfer is even more challenging when moving multilingual.

Olá, Bonjour, Salve! XFORMAL: A Benchmark for Multilingual Formality Style Transfer

Automatic personalized corrective feedback can help language learners from different backgrounds better acquire a new language. This paper introduces a learner English dataset in which learner errors are accompanied by information about possible error sources. This dataset contains manually annotated error causes for learner writing errors. These causes tie learner mistakes to structures from their first languages when the rules in English and in the first language diverge. This new dataset will enable Second Language Acquisition researchers to computationally analyze a large quantity of learner errors that are related to Language Transfer from the learners’ first language. The dataset can also be applied in personalizing grammatical error correction systems according to the learners’ first language and in providing feedback that is informed by the cause of an error

Negative language transfer in learner English: A new dataset

Machine translation (MT) is currently evaluated

SentSim: Crosslingual Semantic Evaluation of Machine Translation

Automatic image captioning has improved significantly over the last few years, but the problem is far from being solved, with state of the art models still often producing low quality captions when used in the wild.

Quality Estimation for Image Captions Based on Large-scale Human Evaluations

Automated systems that negotiate with humans have broad applications in pedagogy and conversational AI. To advance the development of practical negotiation systems, we present CaSiNo: a novel corpus of over a thousand negotiation dialogues in English. Participants take the role of campsite neighbors and negotiate for food, water, and firewood packages for their upcoming trip. Our design results in diverse and linguistically rich negotiations while maintaining a tractable, closed-domain environment. Inspired by the literature in human-human negotiations, we annotate persuasion strategies and perform correlation analysis to understand how the dialogue behaviors are associated with the negotiation performance. We further propose and evaluate a multi-task framework to recognize these strategies in a given utterance. We find that multi-task learning substantially improves the performance for all strategy labels, especially for the ones that are the most skewed. We release the dataset, annotations, and the code to propel future work in human-machine negotiations: https://github.com/kushalchawla/CaSiNo.

CaSiNo: A Corpus of Campsite Negotiation Dialogues for Automatic Negotiation Systems

In this paper, we introduce the task of HeadLine Grouping (HLG) and a corresponding dataset (HLGD) consisting of 20,056 pairs of news headlines, each labeled with a binary judgement as to whether the pair belongs within the same group.

News Headline Grouping as a Challenging NLU Task

Fine-tuning pre-trained language models suchas BERT has become a common practice dom-inating leaderboards across various NLP tasks.Despite its recent success and wide adoption,this process is unstable when there are onlya small number of training samples available.The brittleness of this process is often reflectedby the sensitivity to random seeds. In this pa-per, we propose to tackle this problem basedon the noise stability property of deep nets,which is investigated in recent literature (Aroraet al., 2018; Sanyal et al., 2020). Specifically,we introduce a novel and effective regulariza-tion method to improve fine-tuning on NLPtasks, referred to asLayer-wiseNoiseStabilityRegularization (LNSR). We extend the theo-ries about adding noise to the input and provethat our method gives a stabler regularizationeffect. We provide supportive evidence by ex-perimentally confirming that well-performingmodels show a low sensitivity to noise andfine-tuning with LNSR exhibits clearly highergeneralizability and stability.Furthermore,our method also demonstrates advantages overother state-of-the-art algorithms including L2-SP (Li et al., 2018), Mixout (Lee et al., 2020)and SMART (Jiang et al., 2020).

HTCInfoMax: A Global Model for Hierarchical Text Classification via Information Maximization

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Reading and Acting while Blindfolded: The Need for Semantics in Text Game Agents

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Learning to Decompose and Organize Complex Tasks

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HTCInfoMax: A Global Model for Hierarchical Text Classification via Information Maximization

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Next from NAACL 2021

Reading and Acting while Blindfolded: The Need for Semantics in Text Game Agents

Similar lecture

Learning to Decompose and Organize Complex Tasks

Stay up to date with the latest Underline news!

PRESENTATIONS

CONFERENCES

COMPANY

RESOURCES

Downloads