Singapore

This paper presents the formalization of tree-shape uncertainty that enables us to analyze the inherent branching bias of unsupervised parsing models using raw texts alone. Previous work analyzed the branching bias of unsupervised parsing models by comparing the outputs of trained parsers with gold syntactic trees. However, such approaches do not consider the fact that texts can be generated by different grammars with different syntactic trees, possibly failing to clearly separate the inherent bias of the model and the bias in train data learned by the model. To this end, we formulate tree-shape uncertainty and derive sufficient conditions that can be used for creating texts that are expected to contain no biased information on branching. In the experiment, we show that training parsers on such unbiased texts can effectively detect the branching bias of existing unsupervised parsing models. Such bias may depend only on the algorithm, or it may depend on seemingly unrelated dataset statistics such as sequence length and vocabulary size.

EMNLP 2023

Tree-shape Uncertainty for Analyzing the Inherent Branching Bias of Unsupervised Parsing Models

tree-shape uncertainty

branching bias

probabilistic context-free grammar

unsupervised parsing

workshop paper

**Welcome to EMNLP 2023!**

On behalf of the EMNLP 2023 Organizing Committee, I extend a warm and heartfelt welcome to all of you to the 2023 Conference on Empirical Methods in Natural Language Processing (EMNLP). It is with immense pleasure and excitement that we gather here in Singapore, a vibrant hub of innovation and technological advancement.

The conference program is packed with insightful presentations, thought-provoking workshops, and engaging networking opportunities. In addition to the technical sessions, we have also planned several social events that will provide you with the opportunity to connect with your colleagues.

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Yuji Matsumoto (RIKEN AIP) 
EMNLP 2023 General Chair

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EMNLP 2023 took place in Singapore from Dec 6th to Dec 10th, 2023.

Simultaneous Translation (ST) involves translating with only partial source inputs instead of the entire source inputs, a process that can potentially result in translation quality degradation. Previous approaches to balancing translation quality and latency have demonstrated that it is more efficient and effective to leverage an offline model with a reasonable policy. However, using an offline model also leads to a distribution shift since it is not trained with partial source inputs, and it can be improved by training an additional module that informs us when to translate. In this paper, we propose an Information Quantifier (IQ) that models source and target information to determine whether the offline model has sufficient information for translation, trained with oracle action sequences generated from the offline model. IQ, by quantifying information, helps in formulating a suitable policy for Simultaneous Translation that better generalizes and also allows us to control the trade-off between quality and latency naturally. Experiments on various language pairs show that our proposed model outperforms baselines.

Quantifying Information of Tokens for Simple and Flexible Simultaneous Machine Translation

In this paper we question the almost universal
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should be represented by a single vector. In
fact, it is so natural to use one vector per word
that most people do not even consider it as an
assumption of their various models. Via a series 
of experiments on dependency parsing, in
which we let each token in a sentence be 
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the "one single vector per token" assumption
might be too strong for recurrent neural networks. 
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better when their encoder accesses its input's
tokens' representations in several time steps
rather than all at once. This seems to indicate
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through its vector is too strong a constraint
for recurrent neural networks and calls for further 
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networks.

Challenging the "One Single Vector per Token" Assumption

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PSST! Prosodic Speech Segmentation with Transformers

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Can Language Models Be Tricked by Language Illusions? Easier with Syntax, Harder with Semantics

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Future Lens: Anticipating Subsequent Tokens from a Single Hidden State

Building multi-modal language models has been a trend in the recent years, where additional modalities such as image, video, speech, etc. are jointly learned along with natural languages (i.e., textual information). Despite the success of these multi-modal language models with different modalities, there is no existing solution for neural network architectures and natural languages. Providing neural architectural information as a new modality allows us to provide fast architecture-2-text and text-2-architecture retrieval/generation services on the cloud with a single inference. Such solution is valuable in terms of helping beginner and intermediate ML users to come up with better neural architectures or AutoML approaches with a simple text query. In this paper, we propose ArchBERT, a bi-modal model for joint learning and understanding of neural architectures and natural languages, which opens up new avenues for research in this area. We also introduce a pre-training strategy named Masked Architecture Modeling (MAM) for a more generalized joint learning. Moreover, we introduce and publicly release two new bi-modal datasets for training and validating our methods. The ArchBERT's performance is verified through a set of numerical experiments on different downstream tasks such as architecture-oriented reasoning, question answering, and captioning (summarization). Datasets, codes, and demos are available as supplementary materials.

ArchBERT: Bi-Modal Understanding of Neural Architectures and Natural Languages

Different speakers often produce different names for the same object or entity (e.g., "woman'' vs. "tourist'' for a female tourist). The reasons behind variation in naming are not well understood. We create a Language and Vision dataset for Mandarin Chinese that provides an average of 20 names for 1319 naturalistic images, and investigate how familiarity with a given kind of object relates to the degree of naming variation it triggers across subjects. We propose that familiarity influences naming variation in two competing ways: increasing familiarity can either expand vocabulary, leading to higher variation, or promote convergence on conventional names, thereby reducing variation. We find evidence for both factors being at play. Our study illustrates how computational resources can be used to address research questions in Cognitive Science.

The Impact of Familiarity on Naming Variation: A Study on Object Naming in Mandarin Chinese

There is growing interest in incorporating eye-tracking data and other implicit measures of human language processing into natural language processing (NLP) pipelines. The data from human language processing contain unique insight into human linguistic understanding that could be exploited by language models. However, many unanswered questions remain about the nature of this data and how it can best be utilized in downstream NLP tasks. In this paper, we present EyeStyliency, an eye-tracking dataset for human processing of stylistic text (e.g., politeness). We develop an experimental protocol to collect these style-specific eye movements. We further investigate how this saliency data compares to both human annotation methods and model-based interpretability metrics. We find that while eye-tracking data is unique, it also intersects with both human annotations and model-based importance scores, providing a possible bridge between human- and machine-based perspectives. We propose utilizing this type of data to evaluate the cognitive plausibility of models that interpret style. Our eye-tracking data and processing code are publicly available.

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Human languages are often claimed to fundamentally differ from other communication systems. But what is it exactly that unites them as a separate category? This article proposes to approach this problem – here termed the Zipfian Challenge – as a standard classification task. A corpus with textual material from diverse writing systems and languages, as well as other symbolic and non-symbolic systems, is provided. These are subsequently used to train and test binary classification algorithms, assigning labels "writing" and "non-writing" to character strings of the test sets. The performance is generally high, reaching 98% accuracy for the best algorithms. Human languages emerge to have a statistical fingerprint: large unit inventories, high entropy, and few repetitions of adjacent units. This fingerprint can be used to tease them apart from other symbolic and non-symbolic systems.

Tree-shape Uncertainty for Analyzing the Inherent Branching Bias of Unsupervised Parsing Models

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Quantifying Information of Tokens for Simple and Flexible Simultaneous Machine Translation

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Tree-shape Uncertainty for Analyzing the Inherent Branching Bias of Unsupervised Parsing Models

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Next from EMNLP 2023

Quantifying Information of Tokens for Simple and Flexible Simultaneous Machine Translation

Stay up to date with the latest Underline news!

PRESENTATIONS

CONFERENCES

COMPANY

RESOURCES

Downloads