Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userPredicting emergent linguistic compositions through time: Syntactic frame extension via multimodal chaining
توقع التركيبات اللغوية الناشئة عبر الوقت: ملحق الإطار النحوي عبر تسلسل متعدد الوسائط
306
0 0
0.0
(
0
)
Created by
Shamra Editor
Ask ChatGPT about the research
تعتمد اللغة الطبيعية على معجم محدود للتعبير عن مجموعة غير محدودة من الأفكار الناشئة. هناك نتيجة واحدة لهذا التوتر هي تشكيل مؤلفات جديدة، بحيث يمكن دمج الوحدات اللغوية الحالية مع العناصر الناشئة في تعبيرات جديدة. نحن نطور إطارا يستغل الآليات المعرفية للسلاسل والمعرفة متعددة الوسائط للتنبؤ التعبيرات التركيبية الناشئة عبر الزمن. نقدم نموذج تمديد الإطار النحوي (SFEM) الذي يستمد على نظرية المدعون والمعرفة من الاهتمام "، والفهور"، واللغة "" لاستنتاج كيفية توسيع الأفعال إطاراتها لتشكيل مؤلفات جديدة مع الأسماء الحالية والرواية. نقيم SFEM بصرامة على 1) طرائق المعرفة و 2) تصنيف نماذج من التفصيل، في كوربوس الإنجليزية المحلينة على مدى 150 عاما الماضية. نظرا لأن SFEM Multimodal يتوقع بناء جملة الفعل والجدات التي ظهرت حديثا أفضل بكثير من النماذج المتنافسة باستخدام المعرفة اللغوية أو غير المستمرة البحتة. نجد دعما لوجهة نظر مثالية للسلاسل بدلا من عرض النموذج الأولي والكشف عن كيفية أن يكون النهج المشترك للسلسل متعدد الوسائط أمرا أساسيا لإنشاء استخدام اللغة الحرفية والجازرة بما في ذلك الاستعارة و Methymyy.
Natural language relies on a finite lexicon to express an unbounded set of emerging ideas. One result of this tension is the formation of new compositions, such that existing linguistic units can be combined with emerging items into novel expressions. We develop a framework that exploits the cognitive mechanisms of chaining and multimodal knowledge to predict emergent compositional expressions through time. We present the syntactic frame extension model (SFEM) that draws on the theory of chaining and knowledge from percept'', concept'', and language'' to infer how verbs extend their frames to form new compositions with existing and novel nouns. We evaluate SFEM rigorously on the 1) modalities of knowledge and 2) categorization models of chaining, in a syntactically parsed English corpus over the past 150 years. We show that multimodal SFEM predicts newly emerged verb syntax and arguments substantially better than competing models using purely linguistic or unimodal knowledge. We find support for an exemplar view of chaining as opposed to a prototype view and reveal how the joint approach of multimodal chaining may be fundamental to the creation of literal and figurative language uses including metaphor and metonymy.
References used
https://aclanthology.org/
rate research
Read More
Vision language navigation is the task that requires an agent to navigate through a 3D environment based on natural language instructions. One key challenge in this task is to ground instructions with the current visual information that the agent per
ceives. Most of the existing work employs soft attention over individual words to locate the instruction required for the next action. However, different words have different functions in a sentence (e.g., modifiers convey attributes, verbs convey actions). Syntax information like dependencies and phrase structures can aid the agent to locate important parts of the instruction. Hence, in this paper, we propose a navigation agent that utilizes syntax information derived from a dependency tree to enhance alignment between the instruction and the current visual scenes. Empirically, our agent outperforms the baseline model that does not use syntax information on the Room-to-Room dataset, especially in the unseen environment. Besides, our agent achieves the new state-of-the-art on Room-Across-Room dataset, which contains instructions in 3 languages (English, Hindi, and Telugu). We also show that our agent is better at aligning instructions with the current visual information via qualitative visualizations.
Grammatical gender may be determined by semantics, orthography, phonology, or could even be arbitrary. Identifying patterns in the factors that govern noun genders can be useful for language learners, and for understanding innate linguistic sources o
f gender bias. Traditional manual rule-based approaches may be substituted by more accurate and scalable but harder-to-interpret computational approaches for predicting gender from typological information. In this work, we propose interpretable gender classification models for French, which obtain the best of both worlds. We present high accuracy neural approaches which are augmented by a novel global surrogate based approach for explaining predictions. We introduce auxiliary attributes' to provide tunable explanation complexity.
Models of language trained on very large corpora have been demonstrated useful for natural language processing. As fixed artifacts, they have become the object of intense study, with many researchers probing'' the extent to which they acquire and rea
dily demonstrate linguistic abstractions, factual and commonsense knowledge, and reasoning abilities. Recent work applied several probes to intermediate training stages to observe the developmental process of a large-scale model (Chiang et al., 2020). Following this effort, we systematically answer a question: for various types of knowledge a language model learns, when during (pre)training are they acquired? Using RoBERTa as a case study, we find: linguistic knowledge is acquired fast, stably, and robustly across domains. Facts and commonsense are slower and more domain-sensitive. Reasoning abilities are, in general, not stably acquired. As new datasets, pretraining protocols, and probes emerge, we believe that probing-across-time analyses can help researchers understand the complex, intermingled learning that these models undergo and guide us toward more efficient approaches that accomplish necessary learning faster.
It is now established that modern neural language models can be successfully trained on multiple languages simultaneously without changes to the underlying architecture, providing an easy way to adapt a variety of NLP models to low-resource languages
. But what kind of knowledge is really shared among languages within these models? Does multilingual training mostly lead to an alignment of the lexical representation spaces or does it also enable the sharing of purely grammatical knowledge? In this paper we dissect different forms of cross-lingual transfer and look for its most determining factors, using a variety of models and probing tasks. We find that exposing our LMs to a related language does not always increase grammatical knowledge in the target language, and that optimal conditions for lexical-semantic transfer may not be optimal for syntactic transfer.
While cross-lingual techniques are finding increasing success in a wide range of Natural Language Processing tasks, their application to Semantic Role Labeling (SRL) has been strongly limited by the fact that each language adopts its own linguistic f
ormalism, from PropBank for English to AnCora for Spanish and PDT-Vallex for Czech, inter alia. In this work, we address this issue and present a unified model to perform cross-lingual SRL over heterogeneous linguistic resources. Our model implicitly learns a high-quality mapping for different formalisms across diverse languages without resorting to word alignment and/or translation techniques. We find that, not only is our cross-lingual system competitive with the current state of the art but that it is also robust to low-data scenarios. Most interestingly, our unified model is able to annotate a sentence in a single forward pass with all the inventories it was trained with, providing a tool for the analysis and comparison of linguistic theories across different languages. We release our code and model at https://github.com/SapienzaNLP/unify-srl.
suggested questions
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
