  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.
