ConflLlama: Domain-specific adaptation of large language models for conflict event classification

Named entity (NE) extraction is traditionally applied to written text. Some recent works extend the extraction to broadcast speech but most of these approaches simply cascade a speech-to-text (STT) engine and a named entity tagger without any information sharing between the two. In this paper, we extract named entities from conversational speech and explore approaches to couple the STT and NE extraction beyond a simple cascade. We propose a new approach that adapts the STT language model based on the extracted named entities. This steers the STT to focus on the subset of the vocabulary that is most important to NE extraction. We performed a number of experiments on English conversational speech in the Fisher Corpus with different STT recognition speeds. We show that the language model (LM) adaptation approach increases NE extraction recall and improves NE performance by as much as 15% as measured by F-score, by significantly improving the word error rate (WER) of NE words, with minimal impact on the overall WER.

Dialog act (DA) segmentation in meeting speech is important for meeting understanding.In this paper, we explore speaker adaptation of hidden event language models (LMs) for DA segmentation using the ICSI Meeting Corpus.Speaker adaptation is performed using a linear combination of the generic speakerindependent LM and an LM trained on only the data from individual speakers.We test the method on 20 frequent speakers, on both reference word transcripts and the output of automatic speech recognition.Results indicate improvements for 17 speakers on reference transcripts, and for 15 speakers on automatic transcripts.Overall, the speaker-adapted LM yields statistically significant improvement over the baseline LM for both test conditions.

: Abstract Dialog act (DA) segmentation in meeting speech is important for meeting understanding. In this paper, we explore speaker adaptation of hidden event language models (LMs) for DA segmentation using the ICSI Meeting Corpus. Speaker adaptation is performed using a linear combination of the generic speaker independent LM and an LM trained on only the data from individual speakers. We test the method on 20 frequent speakers, on both reference word transcripts and the output of automatic speech recognition. Results indicate improvements for 17 speakers on reference transcripts, and for 15 speakers on automatic transcripts. Overall, the speaker-adapted LM yields statistically significant improvement over the baseline LM for both test conditions.

Action research is a specific type of scientific research which, in addition to having all the general features of scientific research, also has its own unique characteristics that distinguish it from other types of research. In order to understand action research in political science it is necessary to have knowledge of the main aspects of politics, political phenomena and processes. Every political phenomenon is associated with a particular activity, action, because it (the phenomenon) always represents a certain relation between people, their needs, interests and goals. We can, therefore, conclude that all politicological research is, in its essence, action research. The basic classification of action research in political science according to purpose is one of the more relevant classifications and comprises: action research whose purpose is the creation of scientific basis for political action, and action research which is the instrument of political action. Within these two types, by application of diverse criteria, there exist numerous subtypes of action research. The primary difference between action research and other types of scientific research is in the method of research, as part of the outline of the scientific idea, i.e. the project of research.

We present speech experiments that were carried out to evaluate a topically focusing language model in large vocabulary speech recognition. An ordered topical clustering is first computed as a self-organized mapping of a large document collection. Language models are then trained for each text cluster or for several neighboring clusters. The obtained organized collection of language models is efficiently utilized in continuous speech recognition to concentrate on the model that corresponds closest to the current topic of discussion. The speech recognition experiments are carried out on a novel Finnish speech database. A property of Finnish that is particularly challenging for speech recognition is the extremely fast vocabulary growth that makes many of the standard word-based language modeling methods impractical for large vocabulary tasks.

This paper investigates the effects of unsupervised language model adaptation (LMA) in handwritten Chinese text recognition. For no prior information of recognition text is available, we use a two-pass recognition strategy. In the first pass, the generic language model (LM) is used to get a preliminary result, which is used to choose the best matched LMs from a set of pre-defined domains, then the matched LMs are used in the second pass recognition. Each LM is compressed to a moderate size via the entropy-based pruning, tree-structure formatting and fewer-byte quantization. We evaluated the LMA for five LM types, including both character-level and word-level ones. Experiments on the CASIA-HWDB database show that language model adaptation improves the performance for each LM type in all domains. The documents of ancient domain gained the biggest improvement of character-level correct rate of 5.87 percent up and accurate rate of 6.05 percent up.

ABSTRACTEducation advocates lament the “leaky pipeline” in science, technology, engineering, and math (STEM), whereby students—especially minorities and women—drop out of STEM at successive stages of the educational system. Defining empirical political science as a branch of STEM, this article proposes that undergraduate research in political science can help to patch this leaky pipeline and expand access to scientific skills and habits of mind. I elaborate on three rationales to support my claim: (1) political science is a relatively diverse field of STEM; (2) college primes students to think like (political) scientists; and (3) students often encounter political science research opportunities for the first time as undergraduates, presenting an opportunity for faculty to “catch” those who selected out of STEM after high school. I substantiate my arguments by drawing on enrollment data, archival documents, the theories of John Dewey, and testimonials from former undergraduate researchers. I also recommend ways for political science departments to provide a meaningful STEM education by enhancing research programs.

Previous articleNext article No AccessThe Reapportionment Decisions: A Return to Dogma?A. Spencer HillA. Spencer Hill Search for more articles by this author PDFPDF PLUS Add to favoritesDownload CitationTrack CitationsPermissionsReprints Share onFacebookTwitterLinkedInRedditEmail SectionsMoreDetailsFiguresReferencesCited by The Journal of Politics Volume 31, Number 1Feb., 1969 Sponsored by the Southern Political Science Association Article DOIhttps://doi.org/10.2307/2128566 Views: 3Total views on this site Citations: 4Citations are reported from Crossref Copyright 1969 Southern Political Science AssociationPDF download Crossref reports the following articles citing this article: Gary J. Jacobsohn The "Pragmatic Dogma" of the Political Thicket: The Jurisprudential Paradox of "One Man, One Vote", Polity 9, no.33 (Nov 2016): 279–301.https://doi.org/10.2307/3234224Robert G. Dixon Who is Listening? Political Science Research in Public Law, PS: Political Science & Politics 4, no.0101 (Sep 2013): 19–26.https://doi.org/10.1017/S1049096500002523Robert G. Dixon Who is Listening? Political Science Research in Public Law, PS 4, no.11 (Nov 2022): 19–26.https://doi.org/10.1017/S0030826900602886Robert G. Dixon Who is Listening? Political Science Research in Public Law, PS 4, no.11 (Nov 2022): 19–26.https://doi.org/10.2307/418570

In-domain texts for estimating statistical language models are not easily found for most languages of the world. We present two techniques to take advantage of in-domain text resources in other languages. First, we extend the notion of <i>lexical triggers</i>, which have been used monolingually for language model adaptation, to the cross-lingual problem, permitting the construction of sharper language models for a target-language document by drawing statistics from related documents in a resource-rich language. Next, we show that <i>cross-lingual latent semantic analysis</i> is similarly capable of extracting useful statistics for language modeling. Neither technique requires explicit translation capabilities between the two languages! We demonstrate significant reductions in both perplexity and word error rate on a Mandarin speech recognition task by using these techniques.

Statistical language models improve the performance of speech recognition systems by providing estimates of a priori probabilities of word sequences. The commonly used trigram language models obtain the conditional probability estimate of a word given the previous two words, from a large corpus of text. The text corpus is often a collection of several small diverse segments such as newspaper articles, or conversations on different topics. Knowledge of the current topic could be utilized to adapt the general trigram language models to match that topic closely. For example, an interpolation of the general language model with one built on the topic data could be used. The authors first discuss the adaptation of general trigram language models to a known topic using the minimum discrimination information (MDI) method. They then present results on the switchboard corpus which consists of telephone conversations on several topics.

This paper presents a successful technique for creating a new language model (LM) that adapts the original target LM used by a machine translation (MT) system. This technique is especially useful for situations where there are very scarce resources for training the target side (Spanish Sign Language (LSE) in our case) in order to properly estimate the target LM, the Sign Language Model (SLM), used by the MT system. The technique uses information from the source language, Spanish in our task, and from the phrase-based translation matrix in order to create a new LM, estimated using web frequencies, which adapts the counts of the SLM through the Maximum A Posteriori method (MAP). The corpus consists of common used sentences spoken by an officer when assisting people in applying for, or renewing, the National Identification Document. The proposed technique allows relative reductions of 15.5% on perplexity and 2.7% on WER for translation, which are close to half the maximum performance obtainable when only the LM is optimized. Index Terms: language model adaptation, machine translation, sign language, web counts.

In this paper, we improve morph-based speech recognition system by focusing adaptation efforts on acronyms (ACRs) and foreign proper names (FPNs). An unsupervised language model (LM) adaptation framework based on two-pass decoding is used. Vocabulary adaptation is applied alongside unsupervised LM adaptation. The aim is to improve both language and pronunciation modeling for FPNs and ACRs. A smart selection algorithm is used to find the most likely topically related foreign words and acronyms from in-domain text. New pronunciation rules are generated for the selected words. Different kinds of morpheme adaptation operations are also evaluated on the ACR and FPN candidate words, to ensure optimal results are gained from pronunciation adaptation. Statistically significant improvements in average word error rate (WER), and term error rate (TER), are achieved using a combination of unsupervised LM adaptation with vocabulary adaptation focused on ACRs and FPNs.

Unsupervised adaptation has evolved as a popular approach for tuning the acoustic models of speaker-independent speech recognition systems to specific speakers, speaker groups or channel conditions while making use of only untranscribed data. This study focuses on procedures for unsupervised adaptation of other probabilistic models that are involved in state-of-the-art speech recognizers and on the joint adaptation of multiple knowledge sources. In particular, we outline and evaluate approaches for adapting both the language model and the pronunciation model (lexicon) without supervision. Initial experiments on off-line lecture speech transcription achieved small but promising word error rate improvements with each approach applied separately. The experimental results on the joint application of acoustic, language and pronunciation model adaptation indicate that the individually achievable performance improvements are additive.

Since the reinstatement of political science as an academic discipline in 1980, the Chinese political science scholarship has as much studied China-related issues as adopted from the West a large number of scholarly findings, research methodology, and theories including the modernization theory in the 1980s, the democratic transition theory from the 1990s onwards, and the governance theory in the twenty-first century. Through the academic dialogue between China and the West, the political science research community in China has, by the same token, formulated its own theories on democracy and governance that are derived from China’s experiences. Furthermore, along with the unprecedented transformations throughout the whole society, Chinese political science has equally undergone major changes, of which a case in point can be the calling in this article for a political science research using "China as a method" (For this notion of "China as a Method", although it comes from Yuzo Mizoguchi, a Japanese scholar on China studies, it is also a product of the mutual learning across civilizations and also benefits from the inspiration of "taking the reform as the methodology". The so-called "China as the methodology" not only focuses on "China as the center", basing one’s research on China, answering questions about China, and putting forward China-based propositions; it also means that the ontology, epistemology, and methodological knowledge generated by “China as the center” or “China as a method” can become a theoretical orientation and methodological framework for Chinese political research in the world.). The best way to understand China is to study its political history, as Chinese history is mainly a political one, demonstrating a high degree of historical continuity. For this very reason, we have proposed a historical political science as a method for studying China—an approach that is different from historical sociology. The historical political science is, indeed, not Chinese-centered or -focused, where a comparative historical analysis will be ushered in as well.

Efficient fine-tuning is vital for adapting large language models (LLMs) to downstream tasks. However, it requires non-trivial efforts to implement these methods on different models. We present LLAMAFACTORY, a unified framework that integrates a suite of cutting-edge efficient training methods. It provides a solution for flexibly customizing the fine-tuning of 100+ LLMs without the need for coding through the built-in web UI LLAMABOARD. We empirically validate the efficiency and effectiveness of our framework on language modeling and text generation tasks. It has been released at https://github.com/hiyouga/LLaMA-Factory and received over 25,000 stars and 3,000 forks.