Recently, Flat-LAttice Transformer (FLAT) has achieved great success in Chinese Named Entity Recognition (NER). FLAT performs lexical enhancement by constructing flat lattices, which mitigates the difficulties posed by blurred word boundaries and the lack of word semantics. In FLAT, the positions of starting and ending characters are used to connect a matching word. However, this method is likely to match more words when dealing with long texts, resulting in long input sequences. Therefore, it significantly increases the memory and computational costs of the self-attention module. To deal with this issue, we advocate a novel lexical enhancement method, InterFormer, that effectively reduces the amount of computational and memory costs by constructing non-flat lattices. Furthermore, with InterFormer as the backbone, we implement NFLAT for Chinese NER. NFLAT decouples lexicon fusion and context feature encoding. Compared with FLAT, it reduces unnecessary attention calculations in "word-character" and "word-word". This reduces the memory usage by about 50% and can use more extensive lexicons or higher batches for network training. The experimental results obtained on several well-known benchmarks demonstrate the superiority of the proposed method over the state-of-the-art hybrid (character-word) models.

翻译：最近,Flat-Lattice变异器(FLAT)在中国命名实体识别(NER)中取得了巨大成功。 FLAT通过建造平面拉提器来进行法律强化,这缓解了单词边界模糊和缺少词义语语义造成的困难。在FLAT中,使用起始和结尾字符的位置来连接一个匹配的词。然而,在处理长文本时,这一方法很可能与更多的单词相匹配,导致输入序列长。因此,它大大增加了自读模块的记忆和计算成本。为了解决这个问题,我们提倡一种新型的词汇强化方法,即InterFormer,通过建造非平面拉提器来有效减少计算和记忆成本。此外,在InterFortices作为主干线时,我们用NFLUPLAT来连接一个匹配的词句。NFLATLAT decouples lecuuples culticon conducional 和 condiscrition comme 中,它减少了“word”中的不必要关注计算。这样可以减少记忆使用率,大约50%,并且可以使用较广泛的计算方法,并且可以用来展示数级化的模型,用以展示已获得的模型。